University Of Massachusetts Lowell

NSF Awards · FY 2024 · 2024-09

Edge computing is an emerging paradigm that extends cloud computing by allocating a range of (edge) servers at or near the user to provide necessary networking, storage, and computing services. Cyber-physical systems (CPS) connect physical devices to the Internet and to each other. This research explores the marriage between edge computing and CPS, namely edge-based CPS. It has attracted a tremendous amount of attention from both academia and industry because it is expected to fundamentally change the way people interact with engineered systems. One essential element –the study of fault-tolerant distributed primitives– is still missing in the literature. This research is focused on distributed consensus primitives that allow a collection of nodes to work as a coherent entity in the presence of various failures and cyberattacks. Concretely, there are three interrelated tasks: (i) identifying fundamental properties, limitations, and trade-offs in the context of edge-based CPS; (ii) designing algorithms that automatically adapt under changing conditions and varying cyberattacks to maintain good performance; and (iii) designing and implementing a realistic simulator to study performance under practical scenarios. The expected outcomes will enable efficient and fault-tolerant services for edge-based CPS, allowing more innovation in CPS applications. This research is expected to advance the state of knowledge by identifying a set of principles, fault-tolerance tools, and analyses for designing distributed fault-tolerant primitives. The work will emphasize models and primitives that allow for practical implementation, with plans for realistic demonstrations of their fault-tolerance and performance advantages. Edge computing and CPS are expected to be key components of next-generation infrastructure, so making edge-based CPS fault-tolerant can yield large-scale benefits. Conversely, missed opportunities for improvements caused by a lack of foundational understanding could have significant performance and fault-tolerance consequences. This research helps address these issues by identifying trade-offs and limitations. The proposed research also plans to develop new pedagogical material on distributed systems that the PI plans to teach, both through courses at Boston College and in online tutorials made available to the public. In addition, the research and outreach work will benefit society by providing principles to help industry and academia design fault-tolerant primitives for edge-based CPS; increasing partnerships between academia and industry; involving women, persons with disabilities, and underrepresented groups in STEM; and increasing public scientific literacy and public engagement with science and technology. 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.

NIH Research Projects · FY 2024 · 2024-09

Over the last 30 years, safety and health knowledge has increased and helped reduce fatali�es through secondary preven�on a�er vessel casualty occurs, yet vessel instability at sea has been causally related to over half of commercial fishing vessel casual�es in a persistent rate, annually. Our prior research indicated USCG vessel loss inspec�ons have commonly recommended a rou�ne stability assessment requirement for US commercial fishing vessels. Such data are not used internally to assess progress towards USCG fishing vessel program goals. Therefore, the main goal for this work is to formulate a strategy to effec�vely implement the desired policy and ul�mately to improve vessel stability in the industry. The strategy will be extracted from our analysis of two main data sources: the USCG commercial fishing vessel incident inves�ga�on reports, and key informant interviews of inves�gators and policy and administra�ve stakeholders. This will provide focused, objec�ve aten�on to exis�ng data combined with stakeholder knowledge about how to overcome ins�tu�onal or poli�cal barriers toward implemen�ng a proven primary preven�on policy. By seeking to increase safety and health informa�on sharing, the purpose of our proposed work and collabora�ve partnership aligns with the interests of the NIOSH/USCG coopera�ve agreement as well as the NORA AFF goals. We seek informa�on to answer the following ques�ons: 1. What does the Coast Guard recommend regarding stability? 2. Have those recommenda�ons been acted on? 3. If not, why not? We will conduct semi-structured, key informant interviews of USCG personnel to determine the process or procedure the USCG currently uses to translate their own recommenda�ons into policy. We will use qualita�ve analysis methods to objec�vely clarify the steps necessary to successfully implement such a policy, and to form a report to disseminate to stakeholders. A final report on the cumula�ve key informant interviews and recommenda�ons analysis will inform a strategy to meet shared goals of the coopera�ve agreement through policy implementa�on rather than con�nue to allow the regulatory status quo and persistent loss of life.

NIH Research Projects · FY 2024 · 2024-09

Project Summary: The confinement, localization, and nanoscale organization of biomolecules can dramatically alter their biochemical and physiological properties creating opportunities for in vivo sensing and enhancing their use as therapeutics. While these principles are relatively well-studied for oligonucleotides, which are frequently used as vaccines, antisense therapeutics, and theranostics, they are less well-studied for short biological peptides of similar lengths (10-100s of amino acids). One particularly relevant class of peptides are intrinsically disordered proteins (IDPs), which are highly reconfigurable in response to environmental stimuli, including temperature, pH, ionic strength, and mechanical force. While fundamental studies have revealed some of these effects on solution peptides, understanding these properties for IDPs that are confined or organized at the nanoscale is less well understood. This represents a significant knowledge gap because understanding and controlling the dynamic properties of IDPs at the nanoscale could enable the creation of stimuli-responsive in vivo pH sensors while providing insights into the impact of confinement on clinically relevant IDPs regions. The long- term goal of our work is to develop a comprehensive and forward-looking framework for understanding, and ultimately designing, the biophysical properties of IDP-nanoparticle architecture. The objective of this project is to reveal the sequence-, density-, and stimuli-dependence of IDPs bound to small (~10 nm) spherical nanoparticles, and its ability to influence and control the physical properties of nanoparticle core. The central hypothesis of this work, based upon previous work on DNA-nanoparticle conjugates (Ross) and IDP pH sensitivity (Gage), is that localization and confinement of IDPs onto nanoparticle constructs will influence their structural plasticity to pH, temperature, and ionic strength. Consequently, IDP-nanoparticle conjugates can generated with tunable responsivity to their environment, leveraging the biophysical properties of the IDPs and the physical properties of the inorganic nanoparticle core. The approach for testing our central hypothesis comprises two specific aims: 1) Reveal the distinct biophysical properties of nanoparticle-bound IDPs compared to free-in-solution IDPs; and 2) Determine how IDP sequence and density influences nanoscale optical readouts for sensing ionic strength, temperature, and pH changes. This study is innovative as it represents one of the first approaches to coupling IDPs to nanoparticles and the insights gained from this study are necessary to develop a system that is tunable for specific environmental changes. These studies are a critical first step towards developing new IDP- nanoparticle sensors that will have a range of biomedical applications.

NSF Awards · FY 2024 · 2024-09

In this project, funded by the MPS-LEAPS (Launching Early-Career Academic Pathways) Program and managed by the Broadening Participation (CHE-BP) Program in the Division of Chemistry, Professor Michael B. Ross and his students at the University of Massachusetts Lowell will perform studies focused on the synthesis and characterization of multimetallic post-transition metal-noble metal nanoparticles to advance fundamental understanding of phase nucleation and plasmonic optical properties in multimetallic nanomaterials and to develop efficient electrocatalysts for carbon dioxide reduction. These materials are important for electronics and energy but are often challenging to understand due to their complexity. Professor Ross and his students will develop a systematic approach to understand how post-transition–noble metal nanoparticles form mixed alloy and intermetallic phases in ternary and quaternary metallic nanoparticles, and how the plasmonic optical properties and the carbon dioxide reduction electrocatalysis of these materials can manipulated and controlled. Their studies could expand the understanding of the principles leading to functional multimetallic and phase-mixed nanoparticles and could bring new fundamental insights into plasmonic and electrocatalytic behaviors in multimetallic materials. The research will be integrated with activities supporting a diverse STEM workforce, highlighted by a summer program where incoming undergraduates will perform hands-on synthesis of nanomaterials and receive mentoring supporting a successful career in chemistry. Professor Ross and his students will synthesize and characterize noble-metal-post-transition-metal alloy nanoparticles. The synthesis will be modified to explore many-component metal nanoparticle alloys and a diverse array of post-transition metals will be integrated. The materials will be investigated using high-resolution TEM, advanced X-ray scattering and absorption methods, and UV-visible spectroscopy. Electrochemical and gas chromatography analysis will be used to understand carbon dioxide reduction catalysis. The influence of the nanoparticle composition on the resulting plasmonic and electrochemical properties will help form structure-function relationships that can be generalized for application to a broader set of metal nanoparticle alloys and structures. 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 Awards · FY 2024 · 2024-09

This interdisciplinary program encompasses mathematics, quantum physics, and material science research. Obtaining a rigorous understanding of physical behavior, such as quantum transport, dynamics, and superconductivity, is one of the major goals of modern mathematical physics. It has applications in condensed matter physics and material science. This project aims to study the localization properties of electron matter waves in disordered media via hidden quantum objects. Theoretical and numerical techniques will be designed to identify localized states in disordered semiconductor models and the related areas of materials design and characterization. The project will provide an opportunity for interdisciplinary and inter-institutional collaborations and support education and diversity through the supervision of undergraduate research. This project addresses the eigenvalue problems of Schroedinger operators, which arise in many areas of applied mathematics and computational physics. It is related to many fields in mathematics, such as functional analysis, harmonic analysis, dynamical systems, partial differential equations, and geometry. The project will study open questions, including many models of interest, such as disordered media and fractal lattices. One goal is to establish the theoretical framework of eigenvalue approximation via a hidden effective potential. The project also studies the problem numerically, demonstrating the computational efficiency and accuracy of the eigenvalue approximation. More general results are expected for general graph operators. Another project goal is to study the evolution of a quantum particle subject to stochastic fluctuations. The diffusive scaling and central limit theorem are expected to be the essential tools for studying such phenomena. 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 Awards · FY 2024 · 2024-09

Large networks appear across the natural sciences, from those modeling neurons in the brain to those modeling the internet. Sufficiently large networks – regardless of their origin or complexity – are guaranteed to have organized substructures. The branch of mathematics that makes this idea precise in theory and applications – and the branch of mathematics to which this project belongs – is called Ramsey Theory. The main purpose of this project is the creation and development of Affine Ramsey Theory, a branch of Ramsey Theory focused on the combinatorial and number-theoretic interaction between addition and multiplication. The questions motivating this subject come from Number Theory, while techniques and tools come from Combinatorics and the theory of Dynamical Systems. The development of Affine Ramsey Theory in this project is expected to provide a new set of tools and results to approach outstanding open questions and find novel applications to other areas. This project is specifically designed to help the University of Massachusetts Lowell, a Minority Serving Institution, reach and train the next generation of mathematics students. The PI will lead a two-week, intensive summer program in the topic and advise two students in a year-long research program. The research experiences provided by this project will engage undergraduate students with mathematics outside the classroom and help them develop specialized skills to carry forward into life and work after graduation. Classical Ramsey Theory is additive, in the sense that its theorems can be framed in terms of the natural action of the additive semigroup of the positive integers under addition on itself. Abstractions of those results yield multiplicative analogues concerning the natural action of the multiplicative semigroup of the positive integers under multiplication on itself. An emerging thread in modern Ramsey Theory, Combinatorial Number Theory, and Dynamical Systems seeks to understand the extent to which these additive and multiplicative actions interrelate. This project will develop Affine Ramsey Theory with a focus on the natural action of the affine semigroup – the subsemigroup of the group of affine transformations of the rational numbers that map the positive integers into themselves – on the positive integers. This focus is expected to strengthen what is known about the Ramsey-theoretic relationships between addition and multiplication; provide a conceptual framework for re-framing existing open problems; and suggest new applications and directions for further research. Tools and techniques will come primarily from Topological Dynamics and Ergodic Theory, fields which have proven effective in the last several decades at addressing questions in Ramsey Theory. 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 Awards · FY 2024 · 2024-09

Spider silks display unrivaled material complexity, with individual animals producing multiple high-performance fibers exhibiting an impressive range of strength, extensibility, and adhesion. Unravelling the molecular composition of spider silks within and across species has importance for understanding how this spectrum of desirable properties evolved, and for engineering applications aimed at mimicking these properties. This project will provide a mentored training program to a mid-career Associate Professor at the University of Massachusetts Lowell that will complement her existing background in silk protein evolution with new training in silk protein production and biomaterial characterization. The training will take place at Tufts University, in the lab of a collaborative partner at the leading edge of silk protein biotechnology. The protected research time made possible with this award will propel the Principal Investigator’s career in a new trajectory for sustained professional advancement. In addition to learning novel methods in bioengineering and material sciences, the award will be used to develop and analyze a spider silk protein database. The database will be used to identify promising new leads for biomaterial development. Broader impacts made possible by this award include the development of a new undergraduate course in Silk Biology, integrating animal biology, human culture, and engineering. With the Principal Investigator training in a new discipline, the project will also enhance student research opportunities at the University of Massachusetts Lowell. The resulting silk protein database and publication will be an important resource for biological engineers looking for novel recipes to produce silk-based materials with enhanced properties. Spider silks are primarily composed of different members of a protein family, the diversification of which explains the divergent material properties of each silk type. As they are protein-based, extensive efforts have focused on producing artificial spider silks mimicking their desirable properties using genetic engineering. However, only a few silk proteins from a handful of species have been intensively studied to understand their structure-function relationship. At the same time, the growth of genomics is revealing an unexpected diversity of spider silk proteins, but the functional contributions of these diverse proteins to silk mechanics is largely unknown. This project will integrate advances in evolutionary genomics and protein engineering to address these gaps through two goals. First, the Principal Investigator will engage in mentored training in the lab of a collaborative partner enabling her to characterize the functional role of specific silk proteins in fiber mechanics. The protein expression work will focus on highly similar silk proteins that have independently evolved in distantly related species to test whether similarity at the sequence level translates into mechanical similarity at the fiber level. Work of the project will also be aimed at assembling and analyzing a spider silk protein database to identify additional candidates for further functional investigation, and to provide a public resource that will build interdisciplinary links between the fields of evolutionary genomics and biomaterial engineering. 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 Awards · FY 2024 · 2024-09

With the support of the Chemical Catalysis program in the Division of Chemistry, Professor V. Sara Thoi of Johns Hopkins University and Professor Fanglin Che of University of Massachusetts, Lowell are studying the design of catalytic materials for electrochemical synthesis of organonitrogen compounds. Organonitrogen species are a ubiquitous class of compounds used in a variety of industries, from agriculture to pharmaceuticals. Urea, for instance, is an important fertilizer, but it is industrially derived from a highly energy-intensive process called the Haber-Bosch process. Electrochemical transformation of abundant carbon and nitrogen molecules, such as carbon dioxide and nitrogen gas, to organonitrogen products thus emerges as an attractive approach. Electrochemical synthesis can be conducted at room temperature, ambient pressure, and in water. Moreover, the rise in renewably generated electricity provides a path towards decarbonization of the chemical manufacturing industry. This project uses a combination of computational and experimental chemistry to design, synthesize, and test new catalysts for forming commercially valuable organonitrogen compounds, such as urea, acetamide, and N-methylamines. The expected outcomes are the fundamental knowledge for activating small molecules to form carbon-nitrogen bonds, the identification of design parameters for synthesizing efficient catalysts, and the broader applications of electrochemical synthesis to a new class of commodity chemicals. Additionally, the educational goal of this project is to engage young students in the STEM field via the publicly accessible video series, “Meet the Chemist,” which highlights the unique and diverse paths of undergraduate students to chemical research. Moreover, a novel theory course on applied machine learning for computational catalysis will be developed for undergraduate and graduate students. The scientific and educational merits of this project advance the frontiers of chemical synthesis, promote public engagement between researchers and young students, and align with our national interest for decarbonization. With the support of the Chemical Catalysis program in the Division of Chemistry, Professor V. Sara Thoi of Johns Hopkins University and Professor Fanglin Che of University of Massachusetts, Lowell are studying the design of catalytic materials for electrochemical synthesis of organonitrogen compounds. This project will focus on a class of metal-organic materials called boron imidazolate frameworks (BIFs) for electrochemical C-N coupling to form commercially valuable products, such as urea, acetamide, and N-methylamines. Owing to their synthetic tunability, BIFs provide facile access to a range of isostructural metal-organic materials to identify structure-function relationships, serving as an ideal materials platform for fundamental insights to catalytic mechanisms. This project has three objectives: i) identify the structure-function relationships between the electronics of the BIFs and C-N coupling, ii) observe key intermediates via in situ vibrational spectroscopy, and iii) develop physics-informed machine learning to identify design criteria for new BIF catalysts. Together, this knowledge will be used to explore the scope of C-N coupling products, using inexpensive and abundant carbon and nitrogen precursors such as carbon dioxide, aldehyde, ketones, dinitrogen, nitrite, nitrates, and amines. Additionally, we will expand an existing video series, called “Meet the Chemist,” to highlight the unique and diverse paths of undergraduate students to chemical research. The videos, which are publicly available, are designed to encourage young students to learn that people of all backgrounds can engage in STEM research. Moreover, a novel theory course on applied machine learning for computational catalysis will be developed for undergraduate and graduate students. Along with these broader outreach goals, this project aims to enhance the utility of electrochemical synthesis for a wide range of industrially relevant compounds, thereby creating opportunities to decarbonize the chemical industry. 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.

NIH Research Projects · FY 2026 · 2024-08

Project Summary/Abstract: Candida-associated denture stomatitis (CADS), due to Candida colonization and biofilm formation on the denture surface, is a significant clinical concern and affects up to 67% of denture wearers. Fungal biofilms serve as a reservoir for various oral bacteria that cause oral health problems and systemic infections. Currently, strategies for controlling CADS are clinically challenging and have high reinfection rates, particularly in elderly and/or immunologically- or medically-compromised patients. This proposal is being submitted in response to NIDCR RFA-DE-24-004 and aims to use natural salivary anticandidal peptides/proteins in multilayer coatings on dentures to control CADS. We have previously demonstrated that covalently binding functional polymers, such as poly(methacrylic acid) (PMAA), onto conventional dentures enhances the binding of cationic anticandidal biomolecules, including histatin 5 (H-5), a major salivary polypeptide of the histatin family with high anticandidal activity. The PMAA functionalized denture material maintained the physical/mechanical properties of the original resin. We have further used a layer-by- layer (LBL) self-assembly technology (alternating the deposition of oppositely charged polyelectrolytes) by pairing H-5 with hyaluronic acid (HA), an anionic unsulfated salivary glycosaminoglycan, to form H-5/HA multilayer coatings, resulting in an increase in the H-5 content on the surface of the denture material. Using H-5 as the outermost layer, the LBL self-assembled H-5/HA multilayer coatings inhibited Candida adhesion and had long-lasting (weeks to months) anticandidal effects that blocked Candida biofilm formation. The LBL coatings were stable and remained on the denture surface after extensive mechanical brushing (e.g., >20,000 cycles of brushing). The long-term goal of this project is to use normal salivary components as natural and safe antifungal therapeutics for controlling Candida biofilm formation and managing CADS. The main objectives of this proposal are to evaluate the efficacy of salivary anticandidal peptides/proteins in multilayer coatings for controlling Candida biofilm formation on rat dentures and in a novel rat denture stomatitis model. The specific aims of the proposed research are: (1) to fabricate rat dentures with salivary anticandidal peptide/protein-based LBL multilayer coatings; (2) to evaluate the anticandidal activity and biocompatibility of the functionalized and coated dentures in vitro; and (3) to evaluate the preclinical efficacy of the dentures coated with salivary anticandidal peptide/protein multilayers in vitro and in vivo. If successful, this new denture technology will be the first therapeutic denture using natural salivary components to provide a long-term biofilm-controlling effects, prevent unnecessary drug exposure, and minimize the risk of developing Candida resistance. These anticandidal technologies will not only have a significant impact on clinical dentistry but may also provide new materials for a variety of medical devices (e.g., catheters, endotracheal tubes) and reduce hospital-acquired infections.

NIH Research Projects · FY 2026 · 2024-08

Project Summary Implicit bias is a well-documented contributor to healthcare disparities that lead to poorer health outcomes, higher infant mortality rates, and chronic disease in racial and ethnic minority populations. There exists a clear and critical need to reimagine how we approach engineering training to improve cultural competency and facilitate patient-centered, economical solutions accessible by all people regardless of race and socioeconomic status. Barriers to paradigm change are entrenched in the ways in which we equip clinicians to care for patients and train engineers to create solutions for clinicians. Our long-term goal is to establish an interdisciplinary, team-based approach for training engineers and clinicians to identify and address barriers to medical care and efficiency. The short-term objective of this proposal is to establish a two-year community-centered medical device innovation program that leads to creative, interdisciplinary problem-solving. The program foundation is supported by three specific aims. Aim 1. Establish a pedagogy that fosters creativity and flexibility in problem-solving of community-based medical needs through multidisciplinary clinician-engineering design teams. Aim 2. Emphasize impactful, financially sustainable, and commercializable solutions based on community need as a model for improving accessibility to care for historically marginalized groups. Aim 3. Improvement of recruitment and retention of students from underrepresented groups to increase the diversity of the biomedical engineering workforce pipeline. The program will recruit 75% of students from underrepresented groups in STEM. Design teams formed by engineering and nursing students will be exposed to the continuum of the design arc through community- centered, patient-centered needs finding, courses in medical device design and regulation as well as considerations for product development and entrepreneurship. Importantly, design teams interdisciplinary training focuses on identifying medical need through direct observation and immersion into the community medical system. The design and entrepreneurship experience of the students from problem identification to medical device commercialization will be supported by the biotech incubator Massachusetts Medical Device Development Center, a Medical Device Development Steering Committee, and the Center for Community Research and Engagement. The program’s effectiveness in training students to identify and address barriers to medical care and efficiency, learning of the material, changes in behavior, and program implementation will be assessed by the Center for Program Evaluation. The proposed program will immediately and directly impact the training and development of the biomedical engineering and nursing workforce through multi-disciplinary collaboration and clinical immersion to facilitate patient-centered design thinking.

NSF Awards · FY 2024 · 2024-08

Large-scale chronic recording from across the brain with minimal disruption is an ideal neuroscientific experiment and a BRAIN Initiative goal since it will help us understand brain mechanisms. While small-size minimally-damaging flexible electrodes are the ideal choice for such recordings, they are also the most difficult to use with current surgical methods, due to electrode buckling and labor-intensive handling of these tiny devices during surgery. A full-functioning system to implant multiple types of electrodes across multi-brain regions in an automated buckling-free fashion while integrating with typically used neuroscience lab surgical equipment is critical for expanding brain signal acquisition capabilities. An instrument that circumvents electrode buckling and non-automated insertion will enable researchers to reach deeper brain structures with previously-too-small minimally damaging electrode arrays and conduct currently unfeasible large-scale recordings and stimulations. More importantly, methodologies developed for such a system would be widely applicable since the principles and mechanics can extend to different animal brains, or more broadly to most tissue membrane penetration cases like biopsy and drug delivery. The goal of this project is to develop a piezoelectric inchworm insertion mechanism and integrate it with a 3D-printed skull cap platform to enable currently impractical high-density broad-scale implantation of miniaturized flexible microelectrodes in a fast and extensible machine-controlled manner. Specifically, in this project, to tackle the buckling problem, the inchworm-skull cap system will provide full support above the membranes and insert only the electrodes (no invasive support/shuttle) with less than 100 μm increments through iterative grip-feed-release inchworm motion. To address the automation challenge, the machine-controlled alignment and insertion with micrometer-level precision, speed control, and potential vibration assistance will reduce surgical time, complication risk, and surgeon fatigue – leading to improved outcomes. The project contains three synergistic tasks: (1) develop and prototype a piezoelectric inchworm machine for planar probes (both silicon-based and flexible), (2) build an inchworm with specialized grippers for multi-microwire arrays, and (3) automate the labor-intensive alignment process of arrays/probes to their insertion start location, driven by image processing and feature recognition. Surgical and electrophysiological recording improvements will be quantified and evaluated through animal studies to guide iterative optimization. The results of the project can be found at: http://leichen.info/. 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 Awards · FY 2024 · 2024-07

The effects of carbon dioxide (CO2) increase on vegetation could have important consequences for climate change and its human impacts. Plants contribute to surface humidity through transpiration, the process through which plants draw moisture from the soil and return it to the air through stomates, tiny openings on their leaves. Transpiration accounts for perhaps 60% of evapotranspiration (ET), the sum of transpiration and surface evaporation, thus vegetation changes that affect transpiration can have a variety of effects linked to surface humidity. For instance less transpiration means less evaporative cooling of the surface, leading to higher temperatures combined with drier air and thus greater fire risk. Less transpiration can also mean that more water is retained in the soil, causing greater runoff and flood risk. There can also be long-range effects, as much of the moisture that falls as rain over continental interiors comes from land-based transpiration. CO2-induced transpiration change is a challenging topic because CO2 has both direct and indirect effects on plants. Plants grow through photosynthesis, combining CO2 from the air with water from the soil, thus more CO2 leads directly to more plant growth. This effect, known as CO2 fertilization, increases transpiration by increasing the land fraction covered by leaves. A second direct effect is that plants typically close their stomates when they have absorbed enough CO2 for photosynthesis, thus more CO2 means less transpiration because fewer stomates are open at any time. The stomatal closure effect opposes the fertilization effect so that the net effect is a delicate imbalance of two effects which are both hard to quantify. As for indirect effects, increasing CO2 warms the atmosphere through the greenhouse effect and the warming climate has a variety of consequences for vegetation. Among these is a longer growing season, as spring onset comes earlier and plants lose their leaves later in the year in the middle latitudes. This change in phenology has various consequences, for instance if transpiration begins earlier in the year the soil could dry out by summer, leading to a greater likelihood of drought. Research conducted here examines the direct effects of CO2 fertilization and stomatal closure along with the indirect effect of the longer growing season given the CO2 increase expected over the 21st century. The work is performed using the Community Earth System Model (CESM), in which vegetation is simulated by the Community Land Model (CLM) and the atmosphere is simulated by the Community Atmosphere Model (CAM). The simulations are performed in specialized configurations in which the CO2 increase is only applied in CLM or only applied in CAM. A moisture tagging procedure is used in CAM to trace moisture that falls as rain back to the location where it evaporated or transpired from the land surface in CLM. Impacts of transpiration change on flooding are further explored using the Catchment-based Macro-scale Floodplain (CaMa-Flood) model, and the influence on fire weather is addressed through calculation of the Canadian Forest Fire Weather Index (FWI). The work is of societal as well as scientific interest given the potentially disruptive effects of transpiration change noted above. Current climate and earth system models all show a decrease in ET with increasing CO2 but there is no consensus on the magnitude of the decrease, thus models cannot produce quantitative guidance for stakeholders. The project has educational value through the development of resources to teach students to run CESM and analyze the output on the supercomputer at the NSF National Center for Atmospheric Research (NCAR). The project also develops online tools to help instructors at other universities to set up and effectively use NCAR's Classroom Allocations of supercomputing resources. In addition, the project creates Python-based Jupyter notebooks for teaching undergraduate classes in weather and climate. Finally, the project provides support and training for a graduate student. 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 Awards · FY 2024 · 2024-07

This project is funded by the Pathways to Enable Open-Source Ecosystems (POSE) Program which seeks to harness the power of open-source development for the creation of new technology solutions to problems of national and societal importance. The Collaborative Open-source Manipulation Performance Assessment for Robotics Enhancement (COMPARE) ecosystem enables the robot manipulation community to more effectively conduct research and evaluate system performance, with the goal of enabling the quantitative comparison of approaches. By establishing a new, community-driven, open-source ecosystem for robot manipulation research and benchmarking, the field will move more rapidly towards solutions to the open problems of robot perception and grasping. This is a crucial step to push the field forward as the ineffectiveness of these capabilities limits progress in robot manipulation. Developing the COMPARE ecosystem will produce the first systematic community-driven framework to roadmap the current open-source landscape, develop mechanisms and strategies to improve the field, and establish activities that will actualize these improvements, resulting in a more effective and harmonious ecosystem for robot manipulation. The COMPARE ecosystem will be developed by establishing a community and governance structure that addresses pertinent issues in robot manipulation (e.g., modularity of software, quality control, and testing frameworks), conducting outreach to build participation, and activating the ecosystem through activities such as workshops and competitions. Given the number of open-source products available for robot manipulation – including perception and planning packages, datasets, benchmarking protocols, object sets, and hardware designs – the goal of the ecosystem is to create a greater cohesion and compatibility between these products. This solution will create community-driven standards for components of software pipelines that will allow increased modularity and easier implementation of these products in a way that allows for greater performance quantification. This technology will, in turn, also establish community-driven standards and procedures to encourage and implement future products that improve and evolve the state of robot manipulation research. 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.

NIH Research Projects · FY 2024 · 2023-09

The project describes a community based participatory research (CBPR) approach to engaging the Maine tribes in managing risks associated with environmental contamination. Each community has expressed concerns about the health of their waters and quality of their subsistence foods with particular concerns around Food Sovereignty, defined as “the right of peoples to healthy and culturally appropriate food produced through ecologically sound and sustainable methods, and their right to define their own food and agriculture systems.” Our central hypothesize is that integrating CBPR and participatory science can be used to equip tribal communities with the tools to manage and balance their risks associated with contamination of water and subsistence foods. We aim to, 1. Build capacity for participatory science amongst tribal youth by providing them the tools for sample collection and basic contaminant monitoring, with more advanced analyses conducted in the University labs; 2. Through collaborations between the Wabanaki Public Health and Wellness (WPHW), Tribal Elders and other Indigenous Knowledge Keepers; traditional ecological knowledge (TEK) around subsistence food consumption patterns and use of waterways for fishing and other activities will be documented through recordings and other media; 3. TEK will be incorporated into health risk assessments in order to support culturally appropriate risk communication and risk management strategies for affected communities. WPHW will serve as a central repository for this information, in order to ensure that it is readily accessible to all of Maine’s Tribes. The ultimate goal of this project is to provide the capacity and training within tribal communities to make informed decisions on the health of their traditional foods and waterways. This in turn will provide the foundations for a larger program that will serve the environmental health needs of Maine’s Tribal communities, while at the same time respecting and supporting food sovereignty that can be sustainable over the longer term and in the face of a rapidly changing environment.

NIH Research Projects · FY 2025 · 2023-09

The UMass LIFT (Lowell Innovative Fellows Training) Program, based upon the highly successful UML IMPACT accelerator will be a comprehensive part-time, once per week, semester-long entrepreneurial skills education program run twice per year, focusing on non- research innovation and entrepreneurship, and targeting innovative researchers in the aging and/or Alzheimer’s disease domains. Together with appropriate UMass Chan clinicians with expertise in the aging and/or Alzheimer’s disease domains, UML Manning School of Business faculty, UML Francis College of Engineering faculty, UML Zuckerberg College of Health Sciences faculty, key ecosystem opinion leaders, subject matter experts and relevant industry partners already in our network, the UMass LIFT Program will promote entrepreneurial skills development by providing the education, strategy training and resources to enable researchers to identify and access new career opportunities in the business and industry segments. LIFT will provide valuable training and career development for these biomedical researcher fellows, will expand the Alzheimer’s research workforce and will advance Alzheimer’s diagnostic and therapeutic development. Specifically, the UMass LIFT program will train rising innovative Alzheimer’s Disease focused researchers in biomedical entrepreneurship and drug development. Through formal mentorship from the UMass team, as well as others sourced from our corporate sponsors and vast network, and by engaging with real business opportunities, the program Fellows will create their own development plans and will gain and refine skills in due diligence, preclinical development, intellectual property, financing, competitive analysis and scientific communication. By the end of the program, UMass LIFT fellows will have completed due diligence on a number of diagnostics and therapeutics related to Alzheimer’s Disease and will have completed a pre- clinical to clinical development plan for the top projects. Fellows will also receive the additional benefit of individualized training and will present it to a team comprised of UMass experts and their industry and subject matter expert and investor partners for potential licensing, engagement and investment opportunities.

NIH Research Projects · FY 2025 · 2023-08

Project summary/abstract Most Americans past middle age are taking one or more common drugs, and these drugs may have hidden impacts on their subsequent risk of major health outcomes like cancer or Alzheimer's disease. Discovering such drug effects could improve disease prevention and suggest drug repurposing opportunities. One approach to this end is to follow the outcomes of people taking each common drug, as recorded in health records data. While health data is growing in size and detail, it is noisy and incomplete. There is a critical need for new methods to discover drug effects from existing data sources. We propose novel systematic approaches to assess drug-wide association with cancer or Alzheimer's disease by modeling the health record. One innovative concept is our parallel efforts mining independent genetics data. We hypothesize drug side effects can be predicted by analysis of pathological processes shared between the drug's original use and the side effect disease. Analyzing evidence of shared etiology between health conditions and cancer or dementia, we will test this hypothesis. Our efforts to systematically discover drug effects from independent health data and genetics will strengthen our methods. Combining both types of evidence can also allow critical evaluation of putative drug effects on a late-onset disease. While many data-driven studies leave evaluation of the results to future work, we integrate rigorous evaluations into our methods as a means to improve them and strengthen their findings. If successful, our findings c improve clinical management of cancers and neurodegenerative diseases, illnesses of high

NIH Research Projects · FY 2026 · 2023-08

Social and behavioral determinants of MOUD utilization and opioid overdose In the US, over 192 people die each day due to opioid overdose (OOD), the leading cause of accidental death among adults. Veterans are especially vulnerable to OOD, experiencing rates double those of non-Veterans, due to their higher prevalence of opioid use disorder (OUD). Medication treatment for OUD (MOUD) is the gold standard treatment for opioid use disorder (OUD). However, in the Veterans Health Administration of the Department of Veterans Affairs (VA) and non-VA hospitals, access to MOUD care is lacking. Compounding poor access, many patients who receive buprenorphine discontinue within 1 year, an indicator of suboptimal treatment retention. Social determinants of health are the conditions in which people are born, live, work, and age. Adverse social determinants of health include job insecurity, housing insecurity, financial insecurity, food insecurity, as well as legal, social/familial, transportation, and violence problems. Together with adverse behavioral health factors such as substance use and mental health disorders (e.g., major depression), adverse social and behavioral determinants of health (SBDH) are associated with poorer health, and concurrent adverse SBDH can have compounding negative health consequences. The overarching goal of this proposal is to understand SBDH, biological variables, clinical settings and clinical factors that impact MOUD access and outcomes and to develop predictive models for MOUD outcomes.

NIH Research Projects · FY 2025 · 2023-07

Abstract The state-of-the-art x-ray photon-counting CT (PCCT) generates images in multi-energy bins simultaneously with high spatial resolution and low radiation dose for tissue characterization and material decomposition. FDA has approved the techniques in 2021. Both clinical PCCT and micro-PCCT scanners are now commercially available. This opens a new door to opportunities for functional, cellular, and molecular x-ray imaging with novel contrast agents such as bismuth and gold nanoparticles. However, x-ray photon-counting detectors are not perfect, and it remains challenging to reconstruct high-quality PCCT images for various clinical applications. Over the past several years, deep learning-based tomographic imaging has become a new frontier of image reconstruction. Different from compressive sensing (CS) methods, which totally rely on the prior information in terms of an accurate mathematical constraint, the emerging deep learning-based approach is empowered by big data with which a deep network can be trained for superior tomographic reconstruction. However, a recent study published in PNAS revealed three types of instabilities of deep tomographic reconstruction networks, which are believed to be fundamental due to lack of kernel awareness and “nontrivial to overcome”, but CS-based reconstruction was reported in that study to be stable because of its kernel awareness. Meanwhile, it is hard to collect large amounts of data with ground-truths for supervised network training up to the clinical image quality. To overcome the aforementioned challenges in the context of a clinical trial with PCCT using Medipix detectors, our overall goal is to develop an Unsupervised Deep Learning Approach (UDLA) for few-view and low-dose image reconstruction based on our Analytic Compressive Iterative Deep (ACID) architecture but specific to PCCT data, with much higher spatial resolution and computational efficiency, and without the requirement of ground- truth for training. ACID combines the data-driven power of deep learning, the kernel-awareness of CS, and iterative refinement to deliver image reconstruction results accurately and stably. To achieve our goal, three specific aims are defined as follows. Aim 1: UDLA will be designed, developed, optimized, and integrated into an open-source platform, including a deep end-to-end reconstruction network and an advanced CS module with a multi-constraint model; Aim 2: UDLA will be tested for stability and generalizability, and accelerated via software optimization on a high-performance computing platform; and Aim 3: UDLA will be evaluated and validated in simulation, experiments, and retrospective use of clinical extremity imaging PCCT data. Upon the completion of this project, the UDLA software should have been characterized for clinical extremity imaging using Medpix-based PCCT to outperform contemporary iterative algorithms, without the vulnerabilities of existing deep reconstruction networks and the requirements of ground-truth for network training. In a broader perspective, our approach represents a paradigm shift towards the integration of model-based and data-driven reconstruction methods, and may have a lasting impact on PCCT and other tomographic imaging modalities.

NIH Research Projects · FY 2025 · 2023-05

Abstract Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality, with 19 million deaths globally in 2020 alone. Central to management of CVDs is deepening our knowledge of physiology and pathology of the heart. Major barriers to our greater understanding of the heart include its deep location and fast dynamics. Evidence from invasive coronary angiography indicates that the maximum velocity of cardiac structures is 52.5 mm/s, requiring a scan time of 19.1ms to eliminate motion artifacts. To achieve this temporal resolution with CT is extremely challenging. There have been substantial gains in CT hardware and software over the last decades (whole heart, dual-source, dedicated cardiac CT, and various approaches to cardiac motion compensation with ECG-gating) that have transformed coronary CT angiography into a robust and viable clinical tool. However, owing to the 140ms temporal resolution of current whole heart CT scanners, diagnosis is still challenging in patients who have irregular and/or fast heart rates especially in cases of arrhythmias and tachycardia, which commonly occur in older adults, many of whom exhibit atrial fibrillation. Here we will apply deep learning to radically improve cardiac CT reconstruction by attaining significantly higher spatial resolution, lower radiation exposure, and better image quality on both modern and legacy CT hardware. To improve wide-area-detector cardiac CT performance, we will develop a limited-angle reconstruction algorithm in the Analytic, Compressive, Iterative, and Deep (ACID) reconstruction framework that integrates a deep network trained on large data, sparsity-promotion, analytic modeling, and iterative refinement. For the first time, two of the preeminent advances in signal processing, compressive sensing and deep learning, will be combined to extract full information from scan data and image priors to freeze the beating heart. The specific aims H3 are: (1) Hyper Dataset: Projection datasets in the Radon space and the corresponding ground-truth images without motion artifacts in the image space will be generated in simulation, experiments, and clinical studies; (2) Hybrid Algorithm: A deep learning network and CS-module will be developed, integrated, and accelerated within the ACID framework for limited-angle free-breathing cardiac CT reconstruction, which will be shared on an open-source platform; and (3) Holistic Evaluation: The performance of our reconstruction software will be characterized, the stability and generalizability will be investigated, and task-based clinical applications will be demonstrated, including quantification of stenosis severity, aorta dimensions, and motion artifacts within the clinical setting of individuals with atrial fibrillation, tachycardia, and irregular heart rates. Completion of this project will yield a free-breathing cardiac CT algorithm with the unprecedented temporal resolution of 60ms, a 230% improvement over the state-of-the-art, allowing cardiac CT without clinically-relevant motion artifacts. This represents a major step towards the integration of model-driven and data-driven methods for CT image reconstruction, with a lasting impact on not only CT but also other tomographic modalities.

NIH Research Projects · FY 2026 · 2023-03

Social and behavioral determinants of health and Alzheimer’s Disease: Cohort study of the US military veteran population Alzheimer’s Disease (AD) affects an estimated 5.8 million US adults. Veterans are particularly susceptible to AD due to demographic, clinical, and economic factors. Social determinants of health are the conditions in which people are born, live, work, and age. Adverse social determinants of health include job loss and financial and food insecurity. Together with behavioral health factors (e.g., smoking and substance use) and mental health, adverse social and behavioral determinants of health (SBDH) contribute to adverse health outcomes. Associations between SBDH and AD have been noted, but most studies used structured electronic health record (EHR) or survey data. SBDH are not routinely added to structured EHR. Natural language processing (NLP) approaches can be developed to automatically extract SBDH and their attributes. This application responds to PAR-22-093 and NOT-AG-18-047. The specific aims are: Aim 1: Establish NLP-enriched case definitions of adverse SBDH and AD-related information (e.g., signs and symptoms of cognitive decline), and examine their incidences by first chart-reviewing ~10,000 EHR notes (e.g., primary care, neurology, psychiatric, and social work notes) and then developing and evaluating sophisticated NLP systems for automatically capturing SBDH and AD-related information. Aim 2: Using NLP enriched SBDH as independent variables from a nested case-control design, we will analyze the associations between adverse SBDH and incident AD. We will also evaluate how the associations vary by age, sex, race/ethnicity. We will compare results using NLP-enriched SBDH vs. using structured data (only) SBDH. Hypothesis 1: Patients with adverse SBDH have substantially higher AD risk, after adjusting for potential covariables (e.g., patient-specific demographic and clinical factors). Hypothesis 2: The effects of adverse SBDH on AD risk vary by age, sex and race/ethnicity, after adjusting for covariables (e.g., patient- specific clinical factors). Hypothesis 3: The effects of adverse SBDH on incident AD are likely cumulative and duration-dependent, with more and longer adverse SBDH leading to higher AD risk. Aim 3: Early AD diagnosis may prevent or delay AD development through intervention efforts on SBDH.34 Cognitive decline occurs 4-8 years prior to AD diagnosis.35 We will study whether inclusion of NLP-enriched adverse SBDH and AD-related information helps early AD diagnosis. We will use three types of predictive models: statistical regression, traditional machine learning, and innovative deep learning models.

NIH Research Projects · FY 2026 · 2023-01

PROJECT SUMMARY/ABSTRACT Doxorubicin is routinely prescribed in treatment of various cancers because of its extremely high efficacy. However, its use is severely limited because of its potential to cause irreversible cardiotoxicity. Since cessation of therapy is not viable in cancer patients, there is a need to explore the molecular mechanisms underlying cardiotoxicity to accurately identify risk factors as well as therapeutic targets for effective adjuncts. The primary mechanism by which doxorubicin exerts its cardiotoxic effects is due to preferential accumulation of excess iron in cardiac mitochondria, which generates cytotoxic free radicals, and disruption of cellular and subcellular iron utilization. Thus, chelating excess mitochondrial iron can prevent doxorubicin-induced cardiac dysfunction. Indeed, the only drug approved to treat doxorubicin cardiomyopathy, dexrazoxane, has demonstrated mitochondrial chelation potential. However, dexrazoxane alters topoisomerases, the enzymes responsible for DNA replication and doxorubicin’s pharmacological target, which thereby impairs doxorubicin’s anticancer activity. In addition, dexrazoxane has potential to induce fatal myelosuppression and acute leukemias, which consequently limit its clinical utility. Cancer survivors who subsequently develop cardiomyopathies have the worst survivals among all cardiomyopathies, and timely intervention results in a superior clinical outcome in those survivors treated with cardiotoxic chemotherapy. Thus, there is a major unmet need for mitochondria-specific iron chelators that do not impede doxorubicin’s antitumor activity. Earlier we have demonstrated that hinokitiol, a small molecule with high iron binding affinity and cell permeability, corrects abnormal iron buildup across the membrane caused by genetic deficiency in mitochondrial iron transporters. These findings prompted us to question if hinokitiol could rescue doxorubicin-induced mitochondrial accumulation of iron. Our pilot study has indicated a feasibility that hinokitiol corrects mitochondrial iron overload and improves survival in cardiac cells treated with doxorubicin with no influence on tumor-killing effect of doxorubicin. Thus, we hypothesize that hinokitiol mobilizes excess iron from the cardiac mitochondria and prevents oxidative damage, thereby reversing doxorubicin-induced cardiomyopathy, while preserving doxorubicin’s anticancer activity. The specific aims are to determine: i) mitochondrial iron export after hinokitiol administration, ii) the cardioprotective effect of hinokitiol on doxorubicin-induced cardiotoxicity, and iii) the effect of hinokitiol on the antineoplastic efficacy of doxorubicin using tumor-bearing mice. Our studies will provide a new therapeutic strategy to reverse abnormal accumulation of mitochondrial iron and correct doxorubicin-induced cardiotoxicity without compromising its antineoplastic effects. If successful, this drug can be safely co-administered with doxorubicin as a rescue factor to improve the therapeutic index of doxorubicin along with better clinical outcome. .

NIH Research Projects · FY 2026 · 2022-01

PROJECT SUMMARY Wnt signaling is an evolutionarily conserved pathway that regulates several cellular behaviors such as cell proliferation, survival, differentiation, and migration to promote tissue homeostasis. Of note, the Wg/Wnt signaling pathway is important for tissue patterning and is often deregulated in epithelial cancers. Secreted Wnt ligands are distributed in the extracellular space to promote paracrine and long-range signaling in target cells. These paracrine and long-range functions of Wnts are dependent on extracellular Wnt availability, which in part is dictated by cell-surface glypican, Dally-like protein (Dlp). In this proposal, I will focus on molecular mechanisms that dictate Dlp-mediated regulation of Wnt availability and signaling. Dlp’s role in regulating Wnt signaling has been described as ‘biphasic’: By continual binding and release, Dlp simultaneously promotes long-range signaling and restricts paracrine signaling, ensuring proper ligand availability at both ranges. The Page-McCaw lab established Drosophila germarium, a tissue where oogenesis occurs, as a model to study mechanisms that define Wnt signaling ranges, and identified a novel Dlp/Mmp2 (Matrix Metalloprotease 2) module that modulates paracrine and long-range Wnt signaling in the germarium. Specifically, I found that proteolytic cleavage of Dlp by Mmp2 alters its subcellular localization and function to modulate Wnt availability. Additionally, my preliminary data suggest that Dlp/Mmp2 may regulate Wnt signaling in epithelial tumors to promote tumor growth. In Aim 1, I will investigate the molecular events that occur downstream of proteolytic cleavage of Dlp to modulate Wg/Wnt availability and signaling in the germarium and tumors. The extracellular Wnt distribution is tightly linked with its production and secretion. I found that Dlp can modulate Wg (Wnt-1) production in source cells in the germarium. Wg production in source cells in germaria is tightly regulated and this regulation is crucial for proper oogenesis. Additionally, I found that Dlp interacts with non-ligand proteins (a finding that has not been previously reported) that communicate with intracellular cytoskeletal machinery, potentially to modulate cell adhesion and/or shape. In Aim 2, I will investigate novel mechanisms of how Dlp regulates Wnt ligand production and long-range Wg distribution to facilitate long-range Wg signaling. These investigations will uncover previously unappreciated roles of cell-surface glypicans in regulating Wnt signaling and elucidate novel paradigms of developmental strategies employed in multicellular organisms to maintain tissue homeostasis.

NIH Research Projects · FY 2025 · 2021-08

ABSTRACT Each year more than 3 million craniofacial injuries occur in the US as a result of trauma, combat-associated lesions, tumor removal, congenital abnormalities, and aging. Although some of these conditions can be addressed by using the patient’s own tissues grafted from another site, this approach leaves the patients susceptible to infections and creates additional trauma. Currently available methods for treatment and restoration of craniofacial defects have limitations with the availability of autografts, immune rejection, high cost, inadequate implant characteristics (oxygen content, mechanical properties, porosity, biocompatibility, degradation, infection risks), and lack of vascularization. Bone repair is crucial to restore patient functionality post-injury. Scaffolds that are easy-to-handle, inexpensive, biodegradable, bioactive, and non-immunogenic with adequate porosity and oxygen content as well as proper mechanical strength are highly sought after for repairing craniofacial defects. The choice of the implant material is of critical importance to facilitate recovery of the injured patients. Recently we developed highly porous scaffolds composed of naturally derived polymers and oxygen-generating components. When combined with cell sources that are compatible with the host, these scaffolds can enhance craniofacial tissue healing. We propose to use materials that are easily accessible, porous, tunable, degradable, and biocompatible. We aim to fabricate hybrid hydrogels that are composed of oxygen-generating depots and gelatin, characterize their physical, chemical, and biological properties as well as studying differentiation of cells and vascularization in these composites. Our preliminary findings suggest that the proposed novel composite hydrogels exhibit significantly improved mechanical properties and indicate a favorable in vivo response by subcutaneous implantation in a rat model as well as full regeneration of critical sized cranial defects. In Aim 1, we will synthesize and characterize oxygen-generating biomaterials with optimized performance and characterize them. In Aim 2, we will assess how the oxygen-generating depots affect cell differentiation and osteogenesis, and develop a vascularized osteogenic model as well as evaluating the functionality of these constructs. In Aim 3, we will implant these composite biomaterials into critical size calvarial defects in vivo to induce bone regeneration. We expect that the integration of oxygen-generating depots into photocrosslinkable hydrogels will result in a material with improved mechanical properties and will promote cell growth, differentiation, biomineralization, and vascularization. These composite biomaterials will be suitable for repair or regeneration of craniomaxillofacial tissues. Because oxygen-generating scaffolds will have outstanding tunability, they are expected to be also useful for applications in other tissues such as cartilage. Porous scaffolds with high oxygen content are highly promising materials for creating functional vascularized tissues, and are expected to improve craniomaxillofacial tissue repair and human health.

NIH Research Projects · FY 2024 · 2020-09

Project summary Falls are the leading cause of injuries in older adults. Prevention of fall injuries is a national public health priority. To date, most of the studies on falls in older adults were conducted in non-Hispanic White populations in urban areas. Little is known about the occurrence rates, circumstances and consequences of falls among older adults living in rural and suburban neighborhoods, and among racial/ethnic minorities. To our knowledge, no study on falls has examined how older adults’ space and time use differ in rural, suburban and urban neighborhoods, and how such differences are related to risk of falling. To fill in this knowledge gap, this project will investigate 1) the rural-urban, gender and racial/ethnic differences in time and space use and rates of location- and activity-specific falls; 2) how time and space use influence risks for indoor and outdoor falls among older adults living in urban, suburban and rural neighborhoods; and 3) what personal and neighborhood-level factors are predictive of space and time use and location- and activity-specific falls. Using the integrated data, 4) we will develop personalized prediction models for location- and activity-specific falls. We propose to establish a racially and ethnically diverse, gender-balanced longitudinal cohort of 1,252 adults age 65 years and older in Central Massachusetts, including 500 from urban, 500 from suburban and 252 from rural areas, and 600 (48%) non-Hispanic Whites and 652 (52%) racial/ethnic minorities (218 non-Hispanic Blacks, 218 Hispanics and 216 Asians/other races). Participants will be followed every 6 months for 3 years to track their falls, mobility, activity patterns, disability, health and health behaviors. Fall events will be tracked using monthly falls calendars and follow-up telephone surveys if a fall occurs. Participant mobility patterns with respect to space, frequency and duration will be measured using a global positioning system (GPS) unit, and participant timing, frequency, duration and intensity of indoor and outdoor activities will be concurrently measured using an accelerometer, at baseline, 6, 24 and 30 months. During the follow-up years 2 and 3, participants will be followed using in-home visits, mail or telephone surveys twice a year querying their health habits and health status. The GPS and accelerometer data will be integrated with the participant’s reported health, perception and behavioral data as well as neighborhood environment data. These data will be integrated and analyzed to achieve the above analytic goals. These study results will inform the design of community-based programs for promoting active living and preventing falls that will be effective in both genders, among all racial/ethnic groups and across the rural-urban continuum. Based on the personalized fall risk prediction models to be developed in this study, we will design and test personalized (precision) falls prevention approaches in a subsequent randomized clinical trial study.

NIH Research Projects · FY 2024 · 2020-07

Project Summary/Abstract This renewal application (2020-2025) requests support for the Work Environment program at the University of Massachusetts Lowell to continue its current training program grant with two funded occupational safety and health training programs: a) Occupational Safety/Ergonomics (OS/E) in the College of Engineering; and b) Occupational Epidemiology (Occ Epi) in the College of Health Sciences. These programs have been funded by NIOSH since 1991. They had been housed in the Department of Work Environment until 2016 when that department was merged with another to form the Department of Public Health. In 2018, Drs. Buchholz and Punnett, the Program Directors for each of these two programs, moved to the Department of Biomedical Engineering. The mission of the program, to train work environment professionals and conduct research on the identification and evaluation of safety and health hazards and the design of safe and efficient alternatives, thereby promoting the development of healthy and sustainable workplaces, has not changed. In this mission, we are guided by an overarching vision to: design and promote systems of production that are environmentally-sound, safe, healthy and rewarding for workers, communities, and consumers. Our integrated approach to health and safety strives to answer the basic question, “What is the optimal design of a healthy workplace, and how can it be achieved?” Trainees, regardless of discipline, will be required to complete a three-course core: Work Environment Policy & Practice; Ergonomics & Work and Epidemiology & Biostatistics. In addition, all students will need to complete an occupational health & safety based project. The program’s integrated curriculum has three broad academic objectives: (1) recognition and evaluation of occupational safety and health hazards; (2) control and prevention of occupational safety and health hazards; and (3) development and implementation of workplace interventions including safety and health programs, as well as other social and economic policies. Graduates of the Work Environment program include many successful practitioners and researchers whose careers in occupational safety and health indicate overall success towards these goals. The proposal requests five years of funding to support a total of six graduate trainees: three trainees each in OS/E and Occ Epi. It is expected that we will, in general, support masters trainees for two years, but will also occasionally support doctoral students.