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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Knowledge gaps for neuromorphic ionic computing

BACKGROUND Neuromorphic computing, inspired by the human brain’s ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. Here, we propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and “multichromatic” computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation.

Neuromorphic↗
DOE OSTI2023

Session Introduction: Graph Representations and Algorithms in Biomedicine

Connectivity is a fundamental property of biological systems: on the cellular level, proteins interact with each other to form protein-protein interaction networks (PPIs); on the organism level, neurons are arranged in a network; and on a community-level, species can have complex relationships with one another that drive the development and balance of an ecosystem. Graphs, representations of systems consisting of entities as vertices and their connections as edges, are a useful structure to characterize many such systems. Such models can be used to understand biological systems that naturally have a network structure, including PPIs, biological neurons, and ecosystems. In today’s information age, graph representations and algorithms (often in combination with machine learning techniques) are used to organize massive amounts of related data, much of which may be heterogeneous or unstructured, and identify patterns that represent novel biological insights. PSB’s 2023 session “Graph Representations and algorithms in Biomedicine,” encompasses modern developments in graph theory and its applications to various fields of biomedicine. This session includes a wide range of research - knowledge graphs built from text-mined health data, heterogeneous networks using multi-omic databases, and graphs refined to represent uncertainty or improve memory usage.

Chrisman, Brianna S.↗
DOE OSTI2018

Neuromorphic computing for temporal scientific data classification

In this work, we apply a spiking neural network model and an associated memristive neuromorphic implementation to an application in classifying temporal scientific data. We demonstrate that the spiking neural network model achieves comparable results to a previously reported convolutional neural network model, with significantly fewer neurons and synapses required.

Schuman, Catherine↗
DOE OSTI2017

Community detection with spiking neural networks for neuromorphic hardware

We present results related to the performance of an algorithm for community detection which incorporates event-driven computation. We define a mapping which takes a graph 𝒢 to a system of symmetrically connected, spiking neurons and use spike train similarities to identify vertex communities. On a random graph with 128 vertices and known community structure we show how our approach can be used to identify individual communities from spiking neuron responses.

Hamilton, Kathleen↗
DOE OSTI2014

Regulation and Function of Adult Neurogenesis. From Genes to Cognition

Adult neurogenesis in the hippocampus is a notable process due not only to its uniqueness and potential impact on cognition but also to its localized vertical integration of different scales of neuroscience, ranging from molecular and cellular biology to behavior. Our review summarizes the recent research regarding the process of adult neurogenesis from these different perspectives, with particular emphasis on the differentiation and development of new neurons, the regulation of the process by extrinsic and intrinsic factors, and their ultimate function in the hippocampus circuit. Arising from a local neural stem cell population, new neurons progress through several stages of maturation, ultimately integrating into the adult dentate gyrus network. Furthermore, the increased appreciation of the full neurogenesis process, from genes and cells to behavior and cognition, makes neurogenesis both a unique case study for how scales in neuroscience can link together and suggests neurogenesis as a potential target for therapeutic intervention for a number of disorders.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Triangle Method for Dense ReLU Layers [SWR-25-72]

This software is an implementation of the methods for initializing and training neural networks to be more efficient per parameter, described more fully below and in the related publication: In theory, depth should make a ReLU network EXPONENTIALLY more efficient by enabling it to produce an exponential number of piecewise linear sections in its output. This reasoning is largely based on the work of mathematicians that have hand-constructed networks that make good use of depth. In practice however, even very deep ReLU networks that have been randomly initialized will behave identically to their shallow counterparts - missing an entire exponential dimension of efficiency. The triangle method is a first attempt at realizing the exponential potential of deep networks. Instead of randomly setting weights, we force pairs of neurons in each layer learn to build triangles (i.e. functions from [0,1] -> [0,1] that look like triangles). This is a very efficient pattern for generating lots of linear pieces because composing two triangular functions doubles the number of pieces with each composition. The triangle method is more than just a different initialization, it is a new paradigm of training. Instead of making direct updates to the matrix weights, we do an extra step of backpropagation to collect the derivatives of the loss function with respect to the shapes of the triangles, training them to tilt left or right. This process essentially holds the networks hand throughout the loss landscape and forces it to always use depth effectively by producing triangular shapes internally. This can produce several orders of magnitude of improvement on convex one-dimensional regression problems. Much more theoretical work is needed to realize its full potential beyond this context, but the implementation in this repository will still work in arbitrary numbers of dimensions. The file Triangle_Method.py is a generalized form of the method that will build each neuron its own custom 1-d convex activation function (with exponential efficiency). Example usage on one dimensional problems can be found in Example_Usage.ipynb and an example of using this in a real neural network can be found in Example_VGG16_CIFAR10.ipynb.

Milkert, Max [National Renewable Energy Laboratory↗
DOE OSTI2014

Multiple conserved regulatory domains promote Fezf2 expression in the developing cerebral cortex

The genetic programs required for development of the cerebral cortex are under intense investigation. However, non-coding DNA elements that control the expression of developmentally important genes remain poorly defined. Here we investigate the regulation of Fezƒ2, a transcription factor that is necessary for the generation of deep-layer cortical projection neurons. Results: Using a combination of chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) we mapped the binding of four deep-layer-enriched transcription factors previously shown to be important for cortical development. Building upon this we characterized the activity of three regulatory regions around the Fezƒ2 locus at multiple stages throughout corticogenesis. We identified a promoter that was sufficient for expression in the cerebral cortex, and enhancers that drove reporter gene expression in distinct forebrain domains, including progenitor cells and cortical projection neurons. Conclusions: These results provide insight into the regulatory logic controlling Fezƒ2 expression and further the understanding of how multiple non-coding regulatory domains can collaborate to control gene expression in vivo.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study

Abstract Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe 3 O 4 _A and Fe 3 O 4 _B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe 3 O 4 _A and Fe 3 O 4 _B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Coding and noncoding variants in EBF3 are involved in HADDS and simplex autism

Abstract Background Previous research in autism and other neurodevelopmental disorders (NDDs) has indicated an important contribution of protein-coding (coding) de novo variants (DNVs) within specific genes. The role of de novo noncoding variation has been observable as a general increase in genetic burden but has yet to be resolved to individual functional elements. In this study, we assessed whole-genome sequencing data in 2671 families with autism (discovery cohort of 516 families, replication cohort of 2155 families). We focused on DNVs in enhancers with characterized in vivo activity in the brain and identified an excess of DNVs in an enhancer named hs737. Results We adapted the fitDNM statistical model to work in noncoding regions and tested enhancers for excess of DNVs in families with autism. We found only one enhancer (hs737) with nominal significance in the discovery (p = 0.0172), replication (p = 2.5 × 10 −3 ), and combined dataset (p = 1.1 × 10 −4 ). Each individual with a DNV in hs737 had shared phenotypes including being male, intact cognitive function, and hypotonia or motor delay. Our in vitro assessment of the DNVs showed they all reduce enhancer activity in a neuronal cell line. By epigenomic analyses, we found that hs737 is brain-specific and targets the transcription factor gene EBF3 in human fetal brain. EBF3 is genome-wide significant for coding DNVs in NDDs (missense p = 8.12 × 10 −35 , loss-of-function p = 2.26 × 10 −13 ) and is widely expressed in the body. Through characterization of promoters bound by EBF3 in neuronal cells, we saw enrichment for binding to NDD genes (p = 7.43 × 10 −6 , OR = 1.87) involved in gene regulation. Individuals with coding DNVs have greater phenotypic severity (hypotonia, ataxia, and delayed development syndrome [HADDS]) in comparison to individuals with noncoding DNVs that have autism and hypotonia. Conclusions In this study, we identify DNVs in the hs737 enhancer in individuals with autism. Through multiple approaches, we find hs737 targets the gene EBF3 that is genome-wide significant in NDDs. By assessment of noncoding variation and the genes they affect, we are beginning to understand their impact on gene regulatory networks in NDDs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Accelerating rates of cognitive decline and imaging markers associated with β-amyloid pathology

Objective: Our objective is to estimate points along the spectrum of β-amyloid pathology at which rates of change of several measures of neuronal injury and cognitive decline begin to accelerate. Methods: In 460 patients with mild cognitive impairment (MCI), we estimated the points at which rates of florbetapir PET, fluorodeoxyglucose (FDG) PET, MRI, and cognitive and functional decline begin to accelerate with respect to baseline CSF Aβ 42 . Points of initial acceleration in rates of decline were estimated using mixed-effects regression. Results: Rates of neuronal injury and cognitive and even functional decline accelerate substantially before the conventional threshold for amyloid positivity, with rates of florbetapir PET and FDG PET accelerating early. Temporal lobe atrophy rates also accelerate prior to the threshold, but not before the acceleration of cognitive and functional decline. Conclusions: A considerable proportion of patients with MCI would not meet inclusion criteria for a trial using the current threshold for amyloid positivity, even though on average, they are experiencing cognitive/functional decline associated with prethreshold levels of CSF Aβ 42 . Lastly, future trials in early Alzheimer disease might consider revising the criteria regarding β-amyloid thresholds to include the range of amyloid associated with the first signs of accelerating rates of decline.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Neural correlates of sparse coding and dimensionality reduction

Supported by recent computational studies, there is increasing evidence that a wide range of neuronal responses can be understood as an emergent property of nonnegative sparse coding (NSC), an efficient population coding scheme based on dimensionality reduction and sparsity constraints. We review evidence that NSC might be employed by sensory areas to efficiently encode external stimulus spaces, by some associative areas to conjunctively represent multiple behaviorally relevant variables, and possibly by the basal ganglia to coordinate movement. In addition, NSC might provide a useful theoretical framework under which to understand the often complex and nonintuitive response properties of neurons in other brain areas. Although NSC might not apply to all brain areas (for example, motor or executive function areas) the success of NSC-based models, especially in sensory areas, warrants further investigation for neural correlates in other regions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Assessment of Membrane Fluidity Fluctuations during Cellular Development Reveals Time and Cell Type Specificity

Cell membrane is made up of a complex structure of lipids and proteins that diffuse laterally giving rise to what we call membrane fluidity. During cellular development, such as differentiation cell membranes undergo dramatic fluidity changes induced by proteins such as ARC and Cofilin among others. In this study we used the generalized polarization (GP) property of fluorescent probe Laurdan using two-photon microscopy to determine membrane fluidity as a function of time and for various cell lines. A low GP value corresponds to a higher fluidity and a higher GP value is associated with a more rigid membrane. Four different cell lines were monitored such as hN2, NIH3T3, HEK293 and L6 cells. Membrane fluidity was measured at 12h, 72h and 92 h. Our results show significant changes in membrane fluidity among all cell types at different time points. GP values tend to increase significantly within 92 h in hN2 cells and 72 h in NIH3T3 cells and only at 92 h in HEK293 cells. L6 showed a marked decrease in membrane fluidity at 72 h and starts to increase at 92 h. As expected, NIH3T3 cells have more rigid membrane at earlier time points. On the other hand, neurons tend to have the highest membrane fluidity at early time points emphasizing its correlation with plasticity and the need for this malleability during differentiation. This study sheds light on the involvement of membrane fluidity during neuronal differentiation and development of other cell lines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Neurogenic Effects of Low-Dose Whole-Body HZE (Fe) Ion and Gamma Irradiation

Understanding the dose-toxicity profile of radiation is critical when evaluating potential health risks associated with natural and man-made sources in our environment. This study was done to evaluate the effects of low-dose whole-body high-energy charged (HZE) iron (Fe) ions and low-energy gamma exposure on proliferation and differentiation of adult-born neurons within the dentate gyrus of the hippocampus, cells deemed to play a critical role in memory regulation. To determine the dose-response characteristics of the brain to whole-body Fe-ion vs. gamma-radiation exposure, C57BL/6J mice were irradiated with 1 GeV/n Fe ions or a static 137 Cs source (0.662 MeV) at doses ranging from 0 to 300 cGy. The neurogenesis was analyzed at 48 h and one month postirradiation. These experiments revealed that whole-body exposure to either Fe ions or gamma radiation leads to: 1. An acute decrease in cell division within the dentate gyrus of the hippocampus, detected at doses as low as 30 and 100 cGy for Fe ions and gamma radiation, respectively; and 2. A reduction in newly differentiated neurons (DCX immunoreactivity) at one month postirradiation, with significant decreases detected at doses as low as 100 cGy for both Fe ions and gamma rays. The data presented here contribute to our understanding of brain responses to whole-body Fe ions and gamma rays and may help inform health-risk evaluations related to systemic exposure during a medical or radiologic/nuclear event or as a result of prolonged space travel.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2014

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation and Completion of Episodic Information

Adult neurogenesis in the hippocampus region of the brain is a neurobiological process that is believed to contribute to the brain's advanced abilities in complex pattern recognition and cognition. Here, we describe how realistic scale simulations of the neurogenesis process can offer both a unique perspective on the biological relevance of this process and confer computational insights that are suggestive of novel machine learning techniques. First, supercomputer based scaling studies of the neurogenesis process demonstrate how a small fraction of adult-born neurons have a uniquely larger impact in biologically realistic scaled networks. Second, we describe a novel technical approach by which the information content of ensembles of neurons can be estimated. Finally, we illustrate several examples of broader algorithmic impact of neurogenesis, including both extending existing machine learning approaches and novel approaches for intelligent sensing.

Aimone, James Bradley↗
DOE OSTI2019

Evaluating the organophosphate NIMP on a 3D-brain-on-a-chip system

In the early 1940s, organophosphates used in insecticides were discovered to act as chemical nerve agents. The most common nerve agents are: isopropyl methylphosphonofluoridate (sarin), ethyl dimethylphosphoramidocyanidate (tabun), and S-2-diisopropylaminoethyl O-ethyl methylphosphonothioate (VX). These nerve agents are responsible for causing acetylcholinesterase (AChE) inhibition which leads to acetylcholine gathering in the synapses. This accumulation causes hyperactivity in the nervous system which causes seizures, bradycardia, respiratory paralysis, and eventually death. For this study, the effects of a sarin chemical surrogate, 4-nitrophenyl isopropyl methylphosphonate (NIMP), was evaluated along with the pre-treatment, pyridostigmine bromide (PB) on 3D cultures of human neurons placed on the novel multi-electrode array (MEA). Neuronal spiking and bursting activity was monitored throughout the experiment and levels of cytotoxicity were determined using the lactate dehydrogenase (LDH) assay. In addition, in-vivo analysis was performed for comparison.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Science & Technology Review June 2019

In collaboration with researchers at the University of California at San Francisco (UCSF), Lawrence Livermore scientists developed a novel system for recording widespread brain activity using highdensity, implantable electrode arrays. The research appears in the January 2, 2019, edition of the journal Neuron. As described in the paper, the system is capable of continuously recording the activity of nearly 400 single neurons over a period of at least five months from devices distributed in multiple brain regions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

In-situ X-ray Evaluation of Traumatic Brain Injury Dynamic Impacts (Final Report)

The goal of this project was to create an in-situ model of traumatic brain injury that allows for analysis of chronic traumatic encephalopathy (CTE) development. CTE is a neurodegenerative disease that occurs as a result of traumatic brain injuries. The resulting neurodegeneration and atrophying of cortical brain tissue develop as tau proteins build-up around small blood vessels and at the base of cortical sulci. Aggregation of tau proteins results in blood vessel damage and neurological function loss, leading to cortical tissue atrophy. This occurs only at some neuronal junctions in the brain after trauma. It has been conjectured that the geometrical distribution of force along neurons is responsible for initiation of tau deposition. However, to assess the validity of this proposition, it is necessary to analyze the mechanical response under dynamic loading associated with brain trauma in-situ

42 ENGINEERING↗
DOE OSTI2025

CRCNS US-France Research Proposal: Collaborative Research: Encoding reward expectation in Drosophilia

The fruit fly Drosophila melanogaster has been a valuable model for investigating the genetic and neural bases that underlie learning and memory. Early and most current studies use basic behavior conditioning protocols to study learning in controlled laboratory settings. More recently, the ability to transgenically manipulate many of the brain neurons in the fruit fly with exquisite specificity, and the recent knowledge of the synaptic ‘connectome’ of the fruit fly brain, makes these animals almost unique as a comprehensive model for studies of learning, memory and motivated behavior. In fact, the connectome has revealed many types of new connections that had until now been overlooked. Within this context, the thesis of this proposal is that studies of learning and memory will be greatly enhanced by using more sophisticated means for evaluating memory representations, such as have been developed in vertebrates, and combining those studies with information from the connectome guided by computational modelling. We propose to push beyond the boundaries of existing conditioning protocols for fruit flies to investigate more complex memory representations. In particular, we will investigate the function of reinforcement pathways in relation to the absence of expected reinforcement. More specifically, we propose a series of experiments designed to investigate the memory representations in fruit flies when an expected consequence of a Conditioned Stimulus (CS) fails to occur. Although studies have evaluated how this failure can establish extinction memory for the CS, our studies will go beyond studying extinction. Specifically, we predict that in Drosophila when a CS is associated with a failed expectation of an appetitive food reinforcement it will acquire aversive value, and vice versa for a failed expectation of an aversive reinforcer. We combine these studies with manipulations of reinforcement pathways in the CNS inspired from the connectome, iteratively knitted in with established computational models. Intellectual Merit: The concept of reinforcement expectation and incentive contrast have been influential in the development of studies of associative learning in mammals. These questions are particularly challenging to answer in vertebrates because they require exquisite cellular, temporal, and genetic specificity of experimental manipulations. The recent development of work with identified neurons and their connectomes makes the larval and adult fly brains ripe as models for pushing our understanding of neural bases for these higher- order conditioning phenomena. Broader Impacts: Public health: These analyses and the conceptual framework of prediction error processing underlying them have a profound impact on our understanding of reinforcement-related behavior in humans, including monetary rewards and the mnemonic consequences of traumatic experiences, and for pathologies of the dopamine reinforcement system. Educational: This project will provide interdisciplinary training for postdoctoral researchers, Ph.D. and undergraduate students. The PIs will act as co-supervisors or mentors of students working in the different labs via face-to-face and internet-based technologies. We will also work with ASU’s award-winning Ask- A-Biologist program. This is an online science program designed to enrich the learning experiences of students of all ages and to provide classroom material for use by K-12 teachers. We will develop an extension of a game developed under a prior NSF award, and the new game will include modules to teach K-12 students about how insects learn. We will also integrate into the AAB site a program developed by a collaborator (B Gerber) at the Leibniz Institut für Neurobiologie, Magdeburg, and now in use in schools in Germany, to teach K-12 students how to train animals using the fruit fly larval learning paradigm. Underrepresented groups: All PIs will work with their university offices of Academic Diversity and Equal Opportunity for reaching underrepresented students.

59 BASIC BIOLOGICAL SCIENCES↗