UMBC Student Collection

Permanent URI for this collectionhttp://hdl.handle.net/11603/33

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On Momentum Acceleration for Randomized Coordinate Descent in Matrix Completion
(IEEE, 2025-04) Callahan, Matthew; Vu, Trung; Raich, Raviv
Matrix completion plays an important role in machine learning and signal processing, with applications ranging from recommender systems to image inpainting. Many approaches have been considered to solve the problem and some offer computationally efficient solutions. In particular, a highly-efficient random coordinate descent approach reduces the per-epoch computation dramatically. This paper is concerned with further improvement of computational efficiency to expand the range of problem sizes and conditions that can be solved. Momentum acceleration is a well-known method to improve the efficiency of iterative algorithms, but applying it to random coordinate descent methods without increasing the computational complexity is non-trivial. To address this challenge, we introduce a momentum-accelerated randomized coordinate descent for matrix completion approach that does not increase computational complexity by accelerating at the level of epochs. Additionally, we propose an analysis-driven, tuning-free method for step size selection. To that end, we offer a convergence rate analysis for the algorithm. Using numerical evaluations, we demonstrate the competitiveness of the method and verify the theoretical analysis.
Multi?Scale Spatial Effects Determine Nest Success in Small Urban Forest Patches
(Wiley, 2025-2-28) Ohad, Paris; Studds, Colin
Urban development and resulting habitat fragmentation affect species populations and inter-specific relationships. While urban ecology research often focuses on species distribution and abundance in habitat fragments, less is known about how urban environments affect reproductive success. Here, we show that factors driving songbird nest success in small urban forest patches vary with landscape-specific edge effects and Light Detection and Ranging (LiDAR) derived vegetation structure. Nest success declined within 30 meters of patch edge, but only in more developed urban landscapes. In addition, nest success increased along two fundamental axes of vegetation structure in urban fragments: overstory density and number of ground-to-canopy gaps. Hence, results indicate that forest fragmentation can generate sufficient variation in ecological conditions to create heterogeneity in edge effects and vegetation structure even across the limited urban development gradient. These findings expand to our understanding of fragmentation effects beyond the traditional rural-developed paradigm.
Integrating Frequency-Domain Representations with Low-Rank Adaptation in Vision-Language Models
(2025-03-08) Khan, Md Azim; Gangopadhyay, Aryya; Wang, Jianwu; Erbacher, Robert F.
Situational awareness applications rely heavily on real-time processing of visual and textual data to provide actionable insights. Vision language models (VLMs) have become essential tools for interpreting complex environments by connecting visual inputs with natural language descriptions. However, these models often face computational challenges, especially when required to perform efficiently in real environments. This research presents a novel vision language model (VLM) framework that leverages frequency domain transformations and low-rank adaptation (LoRA) to enhance feature extraction, scalability, and efficiency. Unlike traditional VLMs, which rely solely on spatial-domain representations, our approach incorporates Discrete Fourier Transform (DFT) based low-rank features while retaining pretrained spatial weights, enabling robust performance in noisy or low visibility scenarios. We evaluated the proposed model on caption generation and Visual Question Answering (VQA) tasks using benchmark datasets with varying levels of Gaussian noise. Quantitative results demonstrate that our model achieves evaluation metrics comparable to state-of-the-art VLMs, such as CLIP ViT-L/14 and SigLIP. Qualitative analysis further reveals that our model provides more detailed and contextually relevant responses, particularly for real-world images captured by a RealSense camera mounted on an Unmanned Ground Vehicle (UGV).
Information Theory of Composite Sequence Motifs: Mutational and Biophysical Determinants of Complex Molecular Recognition
(2024-11-15) Mascolo, Elia; Erill, Ivan
The recognition of nucleotide sequence patterns is a fundamental biological process that controls the start sites of replication, transcription and translation, as well as transcriptional and translational regulation. Foundational work on the evolution of biological information showed that the amount of information encoded in the target nucleotide sequence patterns, a quantity named Rsequence, evolves by natural selection to match a predictable quantity called Rfrequency. In this work, we propose a generalization of this canonical framework that can describe composite sequence motifs: motifs composed of a series of sequence patterns at some variable (not necessarily conserved) distance from each other. We find that some information can be encoded through the conservation of the distance between sequence patterns, a quantity we named Rspacer, and that - to be functional - biological systems require the sum of Rsequence and Rspacer to be constant. We empirically validate our mathematical results through evolutionary simulations. We apply this general framework to demonstrate that the pre-recruitment of regulatory complexes to target sites has intrinsic advantages over in situ recruitment in terms of energy dissipation and search efficiency, and that realistic values of protein flexibility co-evolve with the target composite motifs to match their spacer size variability. Lastly, we show that the relative advantage of encoding information in sequence patterns or in spacers depends on the balance between nucleotide substitutions and insertions/deletions, with known estimates for the rates of these mutation types favoring the evolution of composite motifs with highly conserved spacer length.
Impact of Infrastructure Investment on Port Efficiency: A Case Study of Queen Elizabeth II Quay, Sierra Leone
(Scientific Research Publishing, 2025-03-12) Kamara, Tejan Andrew Rollings; Turay, Alpha Uthman; Souldan, Souldan Mohamed; Paul, Dusingize Jean; Smith, Donaldson; Smith, Viorina; Conteh, Alimamy Alpha
This study investigates the impact of infrastructure investment on port efficiency, focusing on Queen Elizabeth II Quay, in Sierra Leone. Using a mixed-methods approach, including Data Envelopment Analysis (DEA), regression analysis, and stakeholder surveys, the study evaluates the effects of infrastructure upgrades on operational efficiency, cargo throughput, and economic growth. Key findings highlight significant improvements, such as a 25% reduction in vessel turnaround times and a 30% increase in annual container throughput, attributed to investments in modern cargo handling equipment, berth expansions, and ICT systems. The study also highlights challenges, like maintenance limitations, insufficient finance, and regulatory inefficiencies, which jeopardize the long-term viability of these enhancements. Environmental factors, such as emissions from enhanced equipment, highlight the necessity of using sustainable technologies. Recommendations highlight the need to fortify public-private partnerships, improve governance structures, and include sustainable practices in forthcoming growth strategies. This study offers practical recommendations for politicians and port authorities, promoting a comprehensive strategy for infrastructure investment that harmonizes operational efficiency, stakeholder contentment, and environmental sustainability.
Impact of increased anthropogenic Amazon wildfires on Antarctic Sea ice melt via albedo reduction
(Cambridge University Press, 2025-03-10) Chakraborty, Sudip; Devnath, Maloy Kumar; Jabeli, Atefeh; Kulkarni, Chhaya; Boteju, Gehan; Wang, Jianwu; Janeja, Vandana
This study shows the impact of black carbon (BC) aerosol atmospheric rivers (AAR) on the Antarctic Sea ice retreat. We detect that a higher number of BC AARs arrived in the Antarctic region due to increased anthropogenic wildfire activities in 2019 in the Amazon compared to 2018. Our analyses suggest that the BC AARs led to a reduction in the sea ice albedo, increased the amount of sunlight absorbed at the surface, and a significant reduction of sea ice over the Weddell, Ross Sea (Ross), and Indian Ocean (IO) regions in 2019. The Weddell region experienced the largest amount of sea ice retreat (~ 33,000 km²) during the presence of BC AARs as compared to ~13,000 km² during non-BC days. We used a suite of data science techniques, including random forest, elastic net regression, matrix profile, canonical correlations, and causal discovery analyses, to discover the effects and validate them. Random forest, elastic net regression, and causal discovery analyses show that the shortwave upward radiative flux or the reflected sunlight, temperature, and longwave upward energy from the earth are the most important features that affect sea ice extent. Canonical correlation analysis confirms that aerosol optical depth is negatively correlated with albedo, positively correlated with shortwave energy absorbed at the surface, and negatively correlated with Sea Ice Extent. The relationship is stronger in 2019 than in 2018. This study also employs the matrix profile and convolution operation of the Convolution Neural Network (CNN) to detect anomalous events in sea ice loss. These methods show that a higher amount of anomalous melting events were detected over the Weddell and Ross regions.
GPT's Devastated and LLaMA's Content: Emotion Representation Alignment in LLMs for Keyword-based Generation
(2025-03-14) Choudhury, Shadab Hafiz; Kumar, Asha; Martin, Lara J.
In controlled text generation using large language models (LLMs), gaps arise between the language model's interpretation and human expectations. We look at the problem of controlling emotions in keyword-based sentence generation for both GPT-4 and LLaMA-3. We selected four emotion representations: Words, Valence-Arousal-Dominance (VAD) dimensions expressed in both Lexical and Numeric forms, and Emojis. Our human evaluation looked at the Human-LLM alignment for each representation, as well as the accuracy and realism of the generated sentences. While representations like VAD break emotions into easy-to-compute components, our findings show that people agree more with how LLMs generate when conditioned on English words (e.g., "angry") rather than VAD scales. This difference is especially visible when comparing Numeric VAD to words. However, we found that converting the originally-numeric VAD scales to Lexical scales (e.g., +4.0 becomes "High") dramatically improved agreement. Furthermore, the perception of how much a generated sentence conveys an emotion is highly dependent on the LLM, representation type, and which emotion it is.
FedMentalCare: Towards Privacy-Preserving Fine-Tuned LLMs to Analyze Mental Health Status Using Federated Learning Framework
(2025-03-14) Sarwar, S. M.
With the increasing prevalence of mental health conditions worldwide, AI-powered chatbots and conversational agents have emerged as accessible tools to support mental health. However, deploying Large Language Models (LLMs) in mental healthcare applications raises significant privacy concerns, especially regarding regulations like HIPAA and GDPR. In this work, we propose FedMentalCare, a privacy-preserving framework that leverages Federated Learning (FL) combined with Low-Rank Adaptation (LoRA) to fine-tune LLMs for mental health analysis. We investigate the performance impact of varying client data volumes and model architectures (e.g., MobileBERT and MiniLM) in FL environments. Our framework demonstrates a scalable, privacy-aware approach for deploying LLMs in real-world mental healthcare scenarios, addressing data security and computational efficiency challenges.
Experimental Demonstration of Turbulence-resistant Lidar via Quantum Entanglement
(2024-05-14) Joshi, Binod; Fitelson, Michael M.; Shih, Yanhua
We report a proof-of-principle experimental demonstration of a turbulence-resistant quantum Lidar system. As a key technology for sensing and ranging, Lidar has drawn considerable attention for a study from quantum perspective, in search of proven advantages complementary to the capabilities of conventional Lidar technologies. Environmental factors such as strong atmospheric turbulence can have detrimental effects on the performance of these systems. We demonstrate the possibility of turbulence-resistant operation of a quantum Lidar system via two-photon interference of entangled photon pairs. Additionally, the reported quantum Lidar also demonstrates the expected noise resistance. This study suggests a potential high precision timing-positioning technology operable under turbulence and noise.
Event-Based Crossing Dataset (EBCD)
(2025-03-21) Mule, Joey; Challagundla, Dhandeep; Saini, Rachit; Islam, Riadul
Event-based vision revolutionizes traditional image sensing by capturing asynchronous intensity variations rather than static frames, enabling ultrafast temporal resolution, sparse data encoding, and enhanced motion perception. While this paradigm offers significant advantages, conventional event-based datasets impose a fixed thresholding constraint to determine pixel activations, severely limiting adaptability to real-world environmental fluctuations. Lower thresholds retain finer details but introduce pervasive noise, whereas higher thresholds suppress extraneous activations at the expense of crucial object information. To mitigate these constraints, we introduce the Event-Based Crossing Dataset (EBCD), a comprehensive dataset tailored for pedestrian and vehicle detection in dynamic outdoor environments, incorporating a multi-thresholding framework to refine event representations. By capturing event-based images at ten distinct threshold levels (4, 8, 12, 16, 20, 30, 40, 50, 60, and 75), this dataset facilitates an extensive assessment of object detection performance under varying conditions of sparsity and noise suppression. We benchmark state-of-the-art detection architectures-including YOLOv4, YOLOv7, EfficientDet-b0, MobileNet-v1, and Histogram of Oriented Gradients (HOG)-to experiment upon the nuanced impact of threshold selection on detection performance. By offering a systematic approach to threshold variation, we foresee that EBCD fosters a more adaptive evaluation of event-based object detection, aligning diverse neuromorphic vision with real-world scene dynamics. We present the dataset as publicly available to propel further advancements in low-latency, high-fidelity neuromorphic imaging: this https URL
Enhancing prosthetic hand control: A synergistic multi-channel electroencephalogram
(Cambridge University Press, 2024-11-28) Maibam, Pooya Chanu; Pei, Dingyi; Olikkal, Parthan Sathishkumar; Vinjamuri, Ramana; Kakoty, Nayan M.
Electromyogram (EMG) has been a fundamental approach for prosthetic hand control. However it is limited by the functionality of residual muscles and muscle fatigue. Currently, exploring temporal shifts in brain networks and accurately classifying noninvasive electroencephalogram (EEG) for prosthetic hand control remains challenging. In this manuscript, it is hypothesized that the coordinated and synchronized temporal patterns within the brain network, termed as brain synergy, contain valuable information to decode hand movements. 32-channel EEGs were acquired from 10 healthy participants during hand grasp and open. Synergistic spatial distribution pattern and power spectra of brain activity were investigated using independent component analysis of EEG. Out of 32 EEG channels, 15 channels spanning the frontal, central and parietal regions were strategically selected based on the synergy of spatial distribution pattern and power spectrum of independent components. Time-domain and synergistic features were extracted from the selected 15 EEG channels. These features were employed to train a Bayesian optimizer-based support vector machine (SVM). The optimized SVM classifier could achieve an average testing accuracy of 94.39 ±± \pm .84% using synergistic features. The paired t-test showed that synergistic features yielded significantly higher area under curve values (p < .05) compared to time-domain features in classifying hand movements. The output of the classifier was employed for the control of the prosthetic hand. This synergistic approach for analyzing temporal activities in motor control and control of prosthetic hands have potential contributions to future research. It addresses the limitations of EMG-based approaches and emphasizes the effectiveness of synergy-based control for prostheses
Energy density enhancement of scalable thermoelectric devices using a low thermal budget method with film thickness variation
(Elsevier, 2024-04-01) Huang, Jiyuan; Ambade, Rohan B.; Lombardo, Jacob; Brooks, Benjamin; Poosapati, Aswani; Banerjee, Priyanshu; Saeidi-Javash, Mortaza; Zhang, Yanliang; Madan, Deepa
Additive manufacturing has been investigated as a more time, energy, and cost-efficient method for fabricating thermoelectric generators (TEGs) compared to traditional manufacturing techniques. Early results have been promising but are held back by including a high-temperature, long-duration curing process to produce high-performance thermoelectric (TE) films. This work investigates the synergistic effect of four factors – a small amount of chitosan binder (0.05wt%), a combination of micron and nano-sized particles, the application of mechanical pressure (20 MPa), and thickness variation (170, 240, 300 µm) – on the performance of stencil printed p-Bi₀.₅Sb₁.₅Te₃ (p-BST) and n-Bi₂Te₂.₇Se₀.₃ (n-BTS) TE composite films. The combination of these four factors controls the micro and nanostructure of the films to decouple their electrical and thermal conductivity effectively. This resulted in figures of merit (ZTs) of 0.89 and 0.5 for p-BST and n-BTS thinner (170 µm) films, respectively, comparable to other additive manufacturing methods despite eliminating the high-temperature, long-duration curing process. The process was also used to fabricate a 6-couple TEG device, which could generate 357.6 µW with a power density of 5.0 mW/cm² at a ∆T of 40 K. The device demonstrated air stability and flexibility for 1000 cycles of bending. Finally, the device was integrated with a voltage step-up converter to power an LED and charge and discharge capacitor at a ∆T of 17 K, demonstrating its applicability as a self-sufficient power source.
Elvis: A Highly Scalable Virtual Internet Simulator
(Inventive Research Organization, 2025-02-13) Boddu, Dheeraj Kumar
Elvis is a highly scalable virtual Internet simulator that can simulate up to a hundred thousand networked machines communicating over TCP/IP on a single off-the-shelf desktop computer. This research describes the construction of Elvis in Rust, a new memory-safe systems programming language, and the design patterns that enabled us to reach scalability targets. Traffic in the simulation is generated from models based on user behavior research and profiling of large web servers. Additionally, a Network Description Language (NDL) was designed to describe large Internet simulations.
Electroencephalogram based Control of Prosthetic Hand using Optimizable Support Vector Machine
(ACM, 2023-11-02) Pooya Chanu, Maibam; Pei, Dingyi; Olikkal, Parthan Sathishkumar; Vinjamuri, Ramana; Kakoty, Nayan M.
Research on electromyogram (EMG) controlled prosthetic hands has advanced significantly, enriching the social and professional lives of people with hand amputation. Even so, the non-functionality of motor neurons in the remnant muscles impedes the generation of EMG as a control signal. However, such people have the same ability as healthy individuals to generate motor cortical activity. The work presented in this paper investigates electroencephalogram (EEG)-based control of a prosthetic hand. EEG of 10 healthy subjects performing the grasping operations were acquired for classification of hand movements. 15 EEG channels were selected to classify hand open and close operations. Hand movement-class-specific time-domain features were extracted from the filtered EEG. A support vector machine (SVM) was employed with 24-fold cross-validation for classification using extracted features. SVM hyper-parameters for the classification model were optimized with a Bayesian optimizer with a minimum prediction error as an objective function. During training and testing of the classifier model, an average accuracy of 96.8 ± 0.98% and 93.4 ± 1.16% respectively, were achieved across the subjects. The trained classifier model was employed to control prosthetic hand open and close operations. This study demonstrates that EEG can be used to control a prosthetic hand by amputees with motor neuron disabilities.
Editorial: Bio-thermal medical devices, methods, and models: new developments and advances
(Frontiers, 2025-03-24) Singh, Manpreet; Bhowmik, Arka; Repaka, Ramjee; Mitra, Kunal
Recent advancements in medical imaging techniques have greatly enhanced the ability to capture anatomically precise and highly detailed vascular structures within biological tissues Singh 2024. This progress is particularly significant for bioheat transfer modeling, where an accurate representation of the vascular network is essential for understanding heat exchange, blood perfusion dynamics, and thermal responses in both healthy and pathological conditions. The integration of high-resolution, three-dimensional geometries extracted from medical imaging data, often at the voxel level, enables more precise simulations, improving the predictive accuracy of thermal treatments and physiological responses Singh 2024. More recently, research efforts have continued to develop anatomically accurate models from medical imaging and develop physics and physiology-based models Singh et al., 2024. On contrary, voxel-based domains generated from medical image are crucial for bioheat transfer modeling; however, a key challenge lies in voxel resolution limitations. Due to the small dimensional scale of blood vessels, not all vessels are captured within a given voxel resolution, resulting in discontinuities in vascular segmentation. Also, pre-capillary vessels such as arterioles, which play a critical role in regulating blood flow resistance, are often modeled within the tissue as a porous domain. Such simplification leads to a loss of critical vascular information, potentially affecting the accuracy of bioheat transfer simulations. Additionally, magnetic particle imaging (MPI) has emerged as a powerful tool for tracking magnetic nanoparticles used in hyperthermia-based cancer treatments. By combining mathematical modeling with MPI, researchers are optimizing nanoparticle induced hyperthermia to improve therapeutic outcomes while minimizing unintended thermal damage to surrounding healthy tissues Singh 2020; Singh et al., 2021; Singh 2023. In this Research Topic, Pawar et al. conducted a sensitivity analysis to assess the impact of the spatial distribution of magnetic iron oxide nanoparticles (MIONs) on tumor temperature. Their study utilized co-registered magnetic resonance (MR)/computed tomography (CT) imaging alongside magnetic particle imaging (MPI) to derive in vivo MION distribution, which was then compared to mathematically generated uniform and Gaussian distributions. Theoretical predictions were based on the Pennes bioheat transfer equation, incorporating the dynamic influence of temperature on blood perfusion. To enhance accuracy, they employed a piecewise function to model the degree of vascular stasis (collapse of vasculature), as previously quantified by Singh 2022 in the context of magnetic hyperthermia. This approach provided valuable insights into optimizing MION distribution for more effective magnetic hyperthermia treatments. In another article of this Research Topic, Amare et al. highlighted the challenges involved in extracting the small blood vessels due to limited resolution of voxels obtained from image data. Their approach clearly provides evidence that mathematical representations of unsegmented blood vessels can approximate the thermal resistance and reduced the need for high-resolution imaging. In addition, their proposed methodology provides a computationally efficient alternative to high-resolution imaging, making it a valuable tool for future applications in biomedical modeling and thermal therapy planning. Besides the above numerical work, Pioletti presented an intriguing and innovative perspective on the role of self-heating in soft tissues, specifically in cartilage, because of mechanical stimulation induced heat effect. The core idea discussed in this work is that temperature changes induced by mechanical activity might be necessary for cartilage maintenance-introduces a potential paradigm shift in how we think about the physiological effects of mechanical loading on musculoskeletal tissues. In addition to the perspective article, Li et al. conducted a bibliometric analysis to assess studies on hypothermia-related injuries, treatment strategies, and underlying mechanisms. This study provides a comprehensive summary of hypothermia's impact on human health and the therapeutic applications of moderate hypothermia. By mapping research trends, frontiers, and key focus areas, the analysis offers valuable insights into the current landscape and future directions of hypothermia research. Additionally, it highlights the distinctions and interconnections between therapeutic and severe hypothermia, offering a clearer understanding of advancements and emerging trends in the field. This Research Topic presents a collection of two research articles, a perspective paper, and a review paper, each showcasing novel discoveries, state-of-the-art advancements, and future directions in the interdisciplinary field of computational modeling in biomedical engineering. These studies emphasize multiscale, multiphysics, and medical imaging-assisted approaches, highlighting their integration and applications. We believe that the insights shared in this collection will pave the way for groundbreaking research in bioheat transfer, accelerating innovations in medical device development.
Developing Advanced Cloud Retrievals for PACE: Building a Joint Spectro-Polarimetric Cloud Microphysics Retrieval
(NASA, 2024-12) Miller, Daniel J.; Meyer, Kerry; Platnick, Steven E.; Zhang, Zhibo; Ademakinwa, Adeleke; Sinclair, Kenneth; Alexandrov, Mikhail; Geogdzhayev, Igor; van Diedenhoven, Bastiaan
Developing a Lagrangian Frame Transformation on Satellite Data to Study Cloud Microphysical Transitions in Arctic Marine Cold Air Outbreaks
(2025-03-13) Seppala, Hannah; Zhang, Zhibo; Zheng, Xue
Arctic marine cold air outbreaks (CAOs) generate distinct and dynamic cloud regimes due to intense air-sea interactions. To understand the temporal evolution of CAO cloud properties and compare different CAO events, a Lagrangian perspective is particularly useful. We developed a novel technique that enables the conversion of inherently Eulerian satellite data into a Lagrangian framework, combining the broad spatiotemporal coverage of satellite observations with the advantages of Lagrangian tracking. This technique was applied to eight CAO cases associated with a recent field campaign. Our results reveal a striking contrast among the cases in terms of cloud-top phase transitions, providing new insights into the evolution of CAO cloud properties.
Descriptor: Smart Event Face Dataset (SEFD)
(IEEE, 2024-10-23) Islam, Riadul; Tummala, Sri Ranga Sai Krishna; Mule, Joey; Kankipati, Rohith; Jalapally, Suraj Kumar; Challagundla, Dhandeep; Howard, Chad; Robucci, Ryan
Smart focal-plane and in-chip image processing has emerged as a crucial technology for vision-enabled embedded systems with energy efficiency and privacy. However, the lack of special datasets providing examples of the data that these neuromorphic sensors compute to convey visual information has hindered the adoption of these promising technologies. Neuromorphic imager variants, including event-based sensors, produce various representations such as streams of pixel addresses representing time and locations of intensity changes in the focal plane, temporal-difference data, data sifted/thresholded by temporal differences, image data after applying spatial transformations, optical flow data, and/or statistical representations. To address the critical barrier to entry, we provide an annotated, temporal-threshold-based vision dataset specifically designed for face detection tasks derived from the same videos used for Aff-Wild2. By offering multiple threshold levels (e.g., 4, 8, 12, and 16), this dataset allows for comprehensive evaluation and optimization of state-of-the-art neural architectures under varying conditions and settings compared to traditional methods. The accompanying tool flow for generating event data from raw videos further enhances accessibility and usability. We anticipate that this resource will significantly support the development of robust vision systems based on smart sensors that can process based on temporal-difference thresholds, enabling more accurate and efficient object detection and localization and ultimately promoting the broader adoption of low-power, neuromorphic imaging technologies. IEEE SOCIETY/COUNCIL Signal Processing Society (SPS) DATA TYPE/LOCATION Images; MD, USA DATA DOI/PID 10.21227/bw2e-dj78
Delivery of Tempol from Polyurethane Nanocapsules to Address Oxidative Stress Post-Injury
(ACS, 2025-02-08) Ale, Temitope; Ale, Tolulope; Baker, Kimberly J.; Zuniga, Kameel M.; Hutcheson, Jack; Lavik, Erin
Traumatic brain injuries (TBIs) result in significant morbidity and mortality due to the cascade of secondary injuries involving oxidative stress and neuroinflammation. The development of effective therapeutic strategies to mitigate these effects is critical. This study explores the fabrication and characterization of polyurethane nanocapsules for the sustained delivery of Tempol, a potent antioxidant. The nanocapsules were designed to extend the release of Tempol over a 30-day period, addressing the prolonged oxidative stress observed post-TBI. Tempol-loaded polyurethane nanocapsules were synthesized using interfacial polymerization and nanoemulsion techniques. Two generations of nanocapsules were produced, differing in Tempol loading and PEGylation levels. The first generation, with lower Tempol loading, exhibited an average size of 159.8 ± 12.61 nm and a Z-average diameter of 771.9 ± 87.95 nm. The second generation, with higher Tempol loading, showed an average size of 141.4 ± 6.13 nm and a Z-average diameter of 560.7 ± 171.1 nm. The zeta potentials were ?18.9 ± 5.02 mV and ?11.9 ± 3.54 mV for the first and second generations, respectively. Both generations demonstrated the presence of urethane linkages, confirmed by Fourier Transform Infrared Spectroscopy (FTIR). Loading studies revealed Tempol concentrations of 61.94 ± 3.04 ?g/mg for the first generation and 77.61 ± 3.04 ?g/mg for the second generation nanocapsules. Release profiles indicated an initial burst followed by a sustained, nearly linear release over 30 days. The higher PEGylation in the second generation nanocapsules is advantageous for intravenous administration, potentially enhancing their therapeutic efficacy in TBI treatment. This study demonstrates the feasibility of using polyurethane nanocapsules for the prolonged delivery of Tempol, offering a promising approach to manage oxidative stress and improve outcomes in TBI patients. Future work will include testing these nanocapsules in vivo to determine their potential at modulating recovery from TBI.
Decoding motor execution and motor imagery from EEG with deep learning and source localization
(Elsevier, 2025-06-01) Kaviri, Sina Makhdoomi; Vinjamuri, Ramana
The use of noninvasive imaging techniques has become pivotal in understanding human brain functionality. While modalities like MEG and fMRI offer excellent spatial resolution, their limited temporal resolution, often measured in seconds, restricts their application in real-time brain activity monitoring. In contrast, EEG provides superior temporal resolution, making it ideal for real-time applications in brain–computer interface systems. In this study, we combined deep learning with source localization to classify two motor task types: motor execution and motor imagery. For motor imagery tasks—left hand, right hand, both feet, and tongue—we transformed EEG signals into cortical activity maps using Minimum Norm Estimation (MNE), dipole fitting, and beamforming. These were analyzed with a custom ResNet CNN, where beamforming achieved the highest accuracy of 99.15%, outperforming most traditional methods. For motor execution involving six types of reach-and-grasp tasks, beamforming achieved 90.83% accuracy compared to 56.39% from a sensor domain approach (ICA + PSD + TSCR-Net). These results underscore the significant advantages of integrating source localization with deep learning for EEG-based motor task classification, demonstrating that source localization techniques greatly enhance classification accuracy compared to sensor domain approaches.

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