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Brown receives DOD grant for nanoformulated delivery of ovarian cancer treatment
“Aggressive metastatic ovarian cancer patients have limited therapy options and clinical trials to evaluate promising alternative combinations have been limited due to the high toxicity. Here we propose a bio/nanoformulation approach to delivery PARPi directly into the peritoneal cavity for sustained release and limited toxicity in combination with innate and adaptive immune modulating nanoparticles of ADU-S100 and anti-PDL1.”
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Champion receives NSF grant to study the interrelation of biology and quantum mechanics
“This work will enhance understanding of how biology integrates quantum behavior into macromolecular function. Proteins and macromolecules have evolved to rely, at least to some degree, on underlying quantum phenomena such as tunneling and spin coherence. The biological outcomes depend on the interplay between the scaffold of the protein (which is often treated classically) and the quantum behavior found within more localized regions of the protein. Investigators within this collaboration will draw upon both experimental and theoretical efforts that focus on hydrogen tunneling and vibrational energy transport in lipoxygenase catalysis as well as on optically excited radical pair formation, spin…
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Tilly uses NSF grant to apply ‘mitochondrial lineage tracing’ to the development of in-vivo mammals
“This project combines our technological advancements in mitochondrial analysis and nanosorting with an in-vivo mitochondrial lineage tracing approach, which will enable us able to map the developmental fate of specific mitochondrial subtypes in mammalian eggs through post-fertilization to the point in early embryogenesis when the inner cell mass and trophectoderm are specified at the first cell-fate decision. This project will also explore the novel concept that subtypes of mitochondria, [differing] in their biochemical properties, proteomic landscapes and segregation patterns during asymmetric cell divisions, serve as transcription factor shuttles that then guide stem cell fate decisions and lineage commitment during development.”
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NIH grant supports data modeling platform for ‘real-time… forecasts of disease activity’
“The objective of this grant is to leverage a wealth of information from a diverse array of data sources to build a modeling platform capable of combining information to produce real-time estimates and forecasts of disease activity (Dengue and Influenza) at multiple geographic scales — nation, state and city — using Brazil as a test case. Additionally, we will use machine learning and mechanistic models to understand disease dynamics at multiple spatial scales, across a heterogeneous country such as Brazil.”
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NSF grant supports study of marsh plant detritus in Merrimack River estuary
“Understanding salt marsh ecosystems is crucial because marsh plants create unique and productive wetland habitat in temperate estuaries for a variety of economically valuable and ecologically important fishes, birds and invertebrates. … In the Merrimack River-Plum Island estuarine system just north of Boston, some salt marshes can receive copious amounts of allochthonous inputs in the form of marsh plant detritus (i.e., large mats of “wrack”), while other salt marshes do not. This project will use aerial imagery, drones and AI to quantify whether wrack accumulates into hierarchically organized hot spots according to predictions based on the oceanography of the system.”
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Yan receives Department of Energy grant to design semiconductor compounds for ‘energy conversion applications’
This project will “develop the data-driven approach based on structure motifs and orbital symmetries to discover and design inorganic semiconductor compounds with optimal electronic structures for energy conversion applications. Objectives are: (i) to develop a framework toward the universal description of structure motifs [and] crystal/orbital symmetries in inorganic compounds; (ii) to accelerate the motif and symmetry based discovery and design of oxide and layered semiconductor compounds with optimal electronic properties for energy conversion applications; iii) to enable the effective learning of structure motifs and orbital symmetries through the combined use of national language processing, graph theory and deep learning.”
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Hughes receives Ramboll US grant to study genetic variation in restored salt marshes
“Genetic variation can be critical for population performance and resilience, yet it is seldom accounted for in habitat restoration efforts. This project will assess the genetic diversity of the dominant salt marsh plants Spartina alterniflora and Spartina patens in natural marshes in and around Belle Isle Marsh, MA. We will also produce local stocks of each of these species from seed and compare their genetic and phenotypic diversity in a common greenhouse environment. This work will inform ongoing and future marsh restoration efforts in Belle Isle and the surrounding region.”
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Manetsch receives NIH grant to research new candidate to treat Chagas disease
“Chagas disease, caused by the protozoan parasite Trypanosoma cruzi, is endemic in the Americas, but has also globalized due to human migration. Despite being one of the major causes of infection-induced heart disease worldwide, current therapies for Chagas disease have inconsistent efficacy and frequent side effects. A major contributor to treatment failure is thought to be the transiently dormant intracellular forms of T. cruzi. … The newly discovered benzoxaborole AN15368 represents the first extensively validated and safe clinical candidate for the treatment of Chagas disease. … This proposal aims to gain additional understanding of this processing step for AN15368 as…
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Wanunu receives NIH grant to develop ‘next-generation single-molecule protein sequencer’
“In this multi-PI project between the Wanunu Lab (Northeastern), Chen Lab (UMass Amherst), Aksimentiev Lab (Urbana Champaign), and Niederweis Lab (U Alabama), we will develop a next-generation single-molecule protein sequencer based on engineered high-resolution nanopores.”
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Photomedical group receives $2.7M grant for ovarian cancer research
“The Spring research group, in collaboration with the Enderling Lab at Moffit Cancer Center, has been awarded a Physical Sciences Oncology Network grant (NCI U01 CA280849; ~$2.7M) titled ‘Fractionated Photoimmunotherapy To Harness Low-Dose Immunostimulation in Ovarian Cancer.’ The project will harness an integrated experimental-mathematical oncology approach to decipher how to best harness immune sparing and immune stimulation of fractionated photoimmunotherapy to personalize treatments for advanced or recurrent ovarian cancer patients with presently dismal survival rates.”
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Levine receives NSF grant to study ‘cell-fate trajectories’
“We propose a joint theoretical/experimental research program to address cell-fate trajectories that occur during induction of EMT, the epithelial-mesenchymal transition. Specifically, recent efforts have indicated that epithelial cells can either undergo direct reprogramming to mesenchymal states or alternatively become more stem-like and exhibit hybrid E/M properties. Based on our preliminary investigations, we will use state-of-the-art single cell measurement technology together with advanced mathematical modelling frameworks to understand how cells choose specific fates and to quantitatively unravel the genetic and epigenetic dynamics that leads these cells along their particular trajectories.”
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How will the ecology of the Baltic Sea be affected by changing ocean conditions?
Katie Lotterhos, professor of marine and environmental sciences, has received funding from the University of Gothenburg for a project titled, “A Seascape of Adaptations: Testing Models That Predict Performance in Multivariate Environments.” The researchers wrote that, “We are studying the adaptation of eelgrass to future ocean conditions in the Baltic Sea.”
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Dong receives NSF funding for ‘Enzyme-Mimicking Catalysts for Cellulose Processing’
“Lignocellulosic biomass from plants is a renewable, carbon-neutral material produced at a scale of 170-200 billion tons per year. The depolymerization of cellulose is a key step in biomass conversion, but it is challenged by the stability and crystalline nature of the cellulose fibers. We will develop synthetic catalysts based on molecularly imprinted nanoparticles that mimic endocellulase, exocellulase and beta-glucosidase for the efficient hydrolysis of cellulose.” The title of the project is “Collaborative Research: Enzyme-Mimicking Catalysts for Cellulose Processing.”
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Brown receives DFCI funding for drug delivery in ‘metastatic, resistant breast cancer’ cases
“The objective of this work is to generate clinically relevant data to support the use of PARPi in combination with local and systemic drug delivery platforms of STING agonists in order to treat metastatic, resistant breast cancer.”
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NIH supports Deming’s research into negative mood and its relation to the brain
“Negative mood is a common feature of anxiety, depression, bipolar disorder, and schizophrenia, [inflicting] immeasurable human suffering. … There is no mechanistic explanation for how negative affect is caused in the brain. A solution to this barrier can be found in predictive processing, an emerging paradigm for unifying brain mechanisms across emotion, cognition, perception, movement and other psychological domains. … I will take advantage of a conceptual innovation from our lab and thirty years of tract-tracing studies in mammals to test the hypothesis that prediction signals and prediction error signals can be traced across specific layers of cerebral cortex and…
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Center for Research Innovation awards $300K to ‘commercially valuable inventions’
“The Spark Fund supports commercially valuable inventions from university researchers in earlier stages of development,” the Center for Research Innovation wrote, “from any field. The goal of the award is to advance a technology or suite of technologies from academia toward commercialization.” Each cycle, award recipients receive a grant up to $50,000. In Fall 2023, those recipients were professors Rebecca Carrier, Eno Ebong, Randall Erb, Leigh Plant, Dori Woods and Yi Zheng. Follow the link to read more about their individual projects.
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‘Why Look at Dead Animals?’ asks Coughlin in provocative taxidermy study
“Lion Attacking a Dromedary was a sensational object for its first viewers at the Paris Universal Exposition in 1867. … As we now know, the Verreaux brothers embedded human remains in the figure of the rider that had formerly been assumed to be just a clothed mannequin. … This essay suggests that theoretical tools derived from Material Ecocriticism and Monster Theory that may help us to think about, or alongside, the affective power of this disturbing taxidermy assemblage, ever aware that this piece draws its power from the theatrical, colonial violence of extraction and extinction.”
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‘Deep Bayesian Active Learning for Accelerating Stochastic Simulation’
“Stochastic simulations such as large-scale, spatiotemporal, age-structured epidemic models are computationally expensive at fine-grained resolution. While deep surrogate models can speed up the simulations, doing so for stochastic simulations and with active learning approaches is an underexplored area. We propose Interactive Neural Process (INP), a deep Bayesian active learning framework for learning deep surrogate models to accelerate stochastic simulations. INP consists of two components, a spatiotemporal surrogate model built upon Neural Process (NP) family and an acquisition function for active learning.” Find the paper and full list of authors in the SIGKDD Conference on Knowledge Discovery and Data Mining proceedings.
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‘Disentangling Node Attributes From Graph Topology for Improved Generalizability in Link Prediction’
“Link prediction is a crucial task in graph machine learning with diverse applications. We explore the interplay between node attributes and graph topology and demonstrate that incorporating pre-trained node attributes improves the generalization power of link prediction models. Our proposed method, UPNA (Unsupervised Pre-training of Node Attributes), solves the inductive link prediction problem by learning a function that takes a pair of node attributes and predicts the probability of an edge, as opposed to Graph Neural Networks, … which can be prone to topological shortcuts in graphs with power-law degree distribution.” Find the paper and full list of authors at…
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‘Can Euclidean Symmetry Be Leveraged in Reinforcement Learning and Planning?’
“In robotic tasks, changes in reference frames typically do not influence the underlying physical properties of the system, which has been known as invariance of physical laws.These changes, which preserve distance, encompass isometric transformations such as translations, rotations, and reflections, collectively known as the Euclidean group. In this work, we delve into the design of improved learning algorithms for reinforcement learning and planning tasks that possess Euclidean group symmetry.” Find the paper and full list of authors at ArXiv.
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‘Leveraging Structure for Improved Classification of Grouped Biased Data’
“We consider semi-supervised binary classification for applications in which data points are naturally grouped … and the labeled data is biased. … The groups overlap in the feature space and consequently the input-output patterns are related across the groups. To model the inherent structure in such data, we assume the partition-projected class-conditional invariance across groups. … We demonstrate that under this assumption, the group carries additional information about the class, over the group-agnostic features, with provably improved area under the ROC curve.” Find the paper and full list of authors in the AAAI Conference on Artificial Intelligence proceedings.
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‘Accelerating Neural MCTS Algorithms Using Neural Sub-Net Structures’
“Neural MCTS algorithms are a combination of Deep Neural Networks and Monte Carlo Tree Search (MCTS) and have successfully trained Reinforcement Learning agents in a tabula-rasa way. … However, these algorithms … take a long time to converge, which requires high computational power and electrical energy. It also becomes difficult for researchers without cutting-edge hardware to pursue Neural MCTS research. We propose Step-MCTS, a novel algorithm that uses subnet structures, each of which simulates a tree that provides a lookahead for exploration.” Find the paper and full list of authors in the International Conference on Autonomous Agents and Multiagent Systems…
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‘Sustainable HCI Under Water: Opportunities for Research with Oceans, Coastal Communities and Marine Systems’
“Although the world’s oceans play a critical role in human well-being, they have not been a primary focus of the sustainable HCI (SHCI) community to date. In this paper, we present a scoping review to show how concerns with the oceans are threaded throughout the broader SHCI literature and to find new research opportunities. We identify several themes that could benefit from focused SHCI research, including marine food sources, culture and coastal communities, ocean conservation, and marine climate change impacts and adaptation strategies.” Find the paper and full list of authors at the Conference on Human Factors in Computing Systems.
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‘Rapid Convergence: The Outcomes of Making PPE During a Healthcare Crisis’
“The U.S. National Institute of Health (NIH) 3D Print Exchange is a public, open-source repository for 3D printable medical device designs with contributions from clinicians, expert-amateur makers, and people from industry and academia. In response to the COVID-19 pandemic, the NIH formed a collection to foster submissions of low-cost, locally manufacturable personal protective equipment (PPE). We evaluated the 623 submissions in this collection … [and] found an immediate design convergence to manufacturing-focused remixes of a few initial designs affiliated with NIH partners and major for-profit groups.” Find the paper and full list of authors at ACM Transactions on Computer-Human Interaction.
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‘OPTIMISM: Enabling Collaborative Implementation of Domain Specific Metaheuristic Optimization’
“For non-technical domain experts and designers it can be a substantial challenge to create designs that meet domain specific goals. This presents an opportunity to create specialized tools that produce optimized designs in the domain. However, implementing domain-specific optimization methods requires a rare combination of programming and domain expertise. … We present OPTIMISM, a toolkit which enables programmers and domain experts to collaboratively implement an optimization component of design tools.” Find the paper and full list of authors in the Conference on Human Factors in Computing Systems proceedings.
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How a deep dive into the internet’s vital protocols earned a ‘Best Paper’ honor
Milton Posner, for the Khoury College of Computer Science, details how “A Formal Analysis of Karn’s Algorithm,” a paper written by professor Cristina Nita-Rotaru, PhD. student Max von Hippel, and “Lenore Zuck at the University of Illinois Chicago, and Ken McMillan at the University of Texas at Austin,” has won a best paper award for its exploration of a protocol important to the basic functioning of the internet.