Browse
Recent Submissions
Now showing 1 - 20 of 1305
Item Validation studies of the FLASH-TV system to passively measure children’s TV viewing(Springer Nature, 2024) Vadathya, Anil Kumar; Garza, Tatyana; Alam, Uzair; Ho, Alex; Musaad, Salma M. A.; Beltran, Alicia; Moreno, Jennette P.; Baranowski, Tom; Haidar, Nimah; Hughes, Sheryl O.; Mendoza, Jason A.; Veeraraghavan, Ashok; Young, Joseph; Sano, Akane; O’Connor, Teresia M.TV viewing is associated with health risks, but existing measures of TV viewing are imprecise due to relying on self-report. We developed the Family Level Assessment of Screen use in the Home (FLASH)-TV, a machine learning pipeline with state-of-the-art computer vision methods to measure children’s TV viewing. In three studies, lab pilot (n = 10), lab validation (n = 30), and home validation (n = 20), we tested the validity of FLASH-TV 3.0 in task-based protocols which included video observations of children for 60 min. To establish a gold-standard to compare FLASH-TV output, the videos were labeled by trained staff at 5-second epochs for whenever the child watched TV. For the combined sample with valid data (n = 59), FLASH-TV 3.0 provided a mean 85% (SD 8%) accuracy, 80% (SD 17%) sensitivity, 86% (SD 8%) specificity, and 0.71 (SD 0.15) kappa, compared to gold-standard. The mean intra-class correlation (ICC) of child’s TV viewing durations of FLASH-TV 3.0 to gold-standard was 0.86. Overall, FLASH-TV 3.0 correlated well with the gold standard across a diverse sample of children, but with higher variability among Black children than others. FLASH-TV provides a tool to estimate children’s TV viewing and increase the precision of research on TV viewing’s impact on children’s health.Item Thermal imaging through hot emissive windows(Springer Nature, 2024) Prasad, Ciril Samuel; Everitt, Henry O.; Naik, Gururaj V.It is not currently possible for an infrared camera to see through a hot window. The window’s own blinding thermal emission prevents objects on the other side from being imaged. Here, we demonstrate a path to overcoming this challenge by coating a hot window with an asymmetrically emitting infrared metasurface whose specially engineered imaginary index of refraction produces an asymmetric spatial distribution of absorption losses in its constituent nanoscale resonators. Operating at 873 K, this metasurface-coated window suppresses thermal emission towards the camera while being sufficiently transparent for thermal imaging, doubling the thermal imaging contrast when compared to a control window at the same temperatureItem Plasma Treatment Technologies for GaN Electronics(MDPI, 2024) Li, Botong; Rahaman, Imteaz; Ellis, Hunter D.; Fu, Houqiang; Zhao, Yuji; Cai, Yong; Zhang, Baoshun; Fu, KaiNowadays, the third-generation semiconductor led by GaN has brought great changes to the semiconductor industry. Utilizing its characteristics of a wide bandgap, high breakdown Electric field, and high electron mobility, GaN material is widely applied in areas such as 5G communication and electric vehicles to improve energy conservation and reduce emissions. However, with the progress in the development of GaN electronics, surface and interface defects have become a main problem that limits the further promotion of their performance and stability, increasing leakage current and causing degradation in breakdown voltage. Thus, to reduce the damage, Plasma treatment technologies are introduced in the fabrication process of GaN electronics. Up to now, designs like the high-resistivity p-GaN cap Layer, passivating termination, and surface recovery process have been established via Plasma treatment, reaching the goals of normally-off transistors, diodes with high breakdown voltage and high-reliability GaN electronics, etc. In this article, hydrogen, fluorine, oxygen, and nitrogen Plasma treatment technologies will be discussed, and their application in GaN electronics will be reviewed and compared.Item Strategic stabilization of arousal boosts sustained attention(Elsevier, 2024) de Gee, Jan Willem; Mridha, Zakir; Hudson, Marisa; Shi, Yanchen; Ramsaywak, Hannah; Smith, Spencer; Karediya, Nishad; Thompson, Matthew; Jaspe, Kit; Jiang, Hong; Zhang, Wenhao; McGinley, Matthew J.Arousal and motivation interact to profoundly influence behavior. For example, experience tells us that we have some capacity to control our arousal when appropriately motivated, such as staying awake while driving a motor vehicle. However, little is known about how arousal and motivation jointly influence decision computations, including if and how animals, such as rodents, adapt their arousal state to their needs. Here, we developed and show results from an auditory, feature-based, sustained-attention task with intermittently shifting task utility. We use pupil size to estimate arousal across a wide range of states and apply tailored signal-detection theoretic, hazard function, and accumulation-to-bound modeling approaches in a large cohort of mice. We find that pupil-linked arousal and task utility both have major impacts on multiple aspects of task performance. Although substantial arousal fluctuations persist across utility conditions, mice partially stabilize their arousal near an intermediate and optimal level when task utility is high. Behavioral analyses show that multiple elements of behavior improve during high task utility and that arousal influences some, but not all, of them. Specifically, arousal influences the likelihood and timescale of sensory evidence accumulation but not the quantity of evidence accumulated per time step while attending. In sum, the results establish specific decision-computational signatures of arousal, motivation, and their interaction in attention. So doing, we provide an experimental and analysis framework for studying arousal self-regulation in neurotypical brains and in diseases such as attention-deficit/hyperactivity disorder.Item Towards objective, temporally resolved neurobehavioral predictors of emotional state(Elsevier, 2024) Kabotyanski, Katherine E.; Yi, Han G.; Hingorani, Rahul; Robinson, Brian S.; Cowley, Hannah P.; Fifer, Matthew S.; Wester, Brock A.; Lamichhane, Bishal; Sabharwal, Ashutosh; Allawala, Anusha B.; Rajesh, Sameer V.; Diab, Nabeel; Mathura, Raissa K.; Pirtle, Victoria; Adkinson, Joshua; Watrous, Andrew J.; Bartoli, Eleonora; Xiao, Jiayang; Banks, Garrett P.; Mathew, Sanjay J.; Goodman, Wayne K.; Pitkow, Xaq; Pouratian, Nader; Hayden, Benjamin Y.; Provenza, Nicole R.; Sheth, Sameer A.Item Technology and health inequities in diabetes care: How do we widen access to underserved populations and utilize technology to improve outcomes for all?(Wiley, 2024) Ebekozien, Osagie; Fantasia, Kathryn; Farrokhi, Farnoosh; Sabharwal, Ashutosh; Kerr, DavidAbstract Digital health technologies are being utilized increasingly in the modern management of diabetes. These include tools such as continuous glucose monitoring systems, connected blood glucose monitoring devices, hybrid closed-loop systems, smart insulin pens, telehealth, and smartphone applications (apps). Although many of these technologies have a solid evidence base, from the perspective of a person living with diabetes, there remain multiple barriers preventing their optimal use, creating a digital divide. In this article, we describe many of the origins of these barriers and offer recommendations on widening access to digital health technologies for underserved populations living with diabetes to improve their health outcomes.Item A frequency-agile retrodirective tag for large-scale sub-terahertz data backscattering(Springer Nature, 2024) Kludze, Atsutse; Kono, Junichiro; Mittleman, Daniel M.; Ghasempour, YasamanBackscattering is a promising power-efficient communication technique providing sustainable wireless links with a low carbon footprint. This approach is a critical enabler for dense IoT networks, which are forecast to grow to 41 billion by 2025. However, existing backscatter designs are limited to the sub-6 GHz bands or narrowband operation in the millimeter-wave regime; therefore, they fail to concurrently support many interference-free low-power users. Enabling a frequency-agile wideband backscatter design in the sub-terahertz offers a two-pronged advantage for densely deployed backscatter networks: spatial reuse enabled by directionality and frequency multiplexing enabled by the large available bandwidth. We present the first sub-THz backscatter architecture that operates above 100 GHz. Our design relies on a detailed understanding of reciprocity in leaky-wave devices and offers a realistic joint localization and communication protocol for sub-THz backscatter networks.Item Evaluating HbA1c-to-average glucose conversion with patient-specific kinetic models for diverse populations(Springer Nature, 2024) Sato Imuro, Sandra Emi; Sabharwal, Ashutosh; Bevier, Wendy; Kerr, DavidThe discrepancy between estimated glycemia from HbA1c values and actual average glucose (AG) levels has significant implications for treatment decisions and patient understanding. Factors contributing to the gap include red blood cell (RBC) lifespan and glucose uptake into the RBC. Personalized models have been proposed to enhance AG prediction accuracy by considering interpersonal variation. This study contributes to our understanding of personalized models for estimating AG from HbA1c. Utilizing data from seven studies (340 participants), including Hispanic/Latino populations with or at risk of non-insulin-treated type 2 diabetes (T2D), we examined kinetic features across cohorts. Additionally, the study simulated scenarios to understand data requirements for improving accuracy. Personalized approaches improved agreement between AG estimations and CGM-AG, particularly with four or more weeks of training CGM data. A multiple linear regression model using kinetic parameters and added clinical features was shown to improve the accuracy of personalized models further. As CGM usage extends beyond type 1 diabetes, there is growing interest in leveraging CGM data for clinical decision-making. Patient-specific models offer a valuable tool for managing glycemic status in patients with discordant HbA1c and AG values.Item Sharp-peaked lanthanide nanocrystals for near-infrared photoacoustic multiplexed differential imaging(Springer Nature, 2024) Loh, Kang Yong; Li, Lei S.; Fan, Jingyue; Goh, Yi Yiing; Liew, Weng Heng; Davis, Samuel; Zhang, Yide; Li, Kai; Liu, Jie; Liang, Liangliang; Feng, Minjun; Yang, Ming; Zhang, Hang; Ma, Ping’an; Feng, Guangxue; Mu, Zhao; Gao, Weibo; Sum, Tze Chien; Liu, Bin; Lin, Jun; Yao, Kui; Wang, Lihong V.; Liu, XiaogangPhotoacoustic tomography offers a powerful tool to visualize biologically relevant molecules and understand processes within living systems at high resolution in deep tissue, facilitated by the conversion of incident photons into low-scattering acoustic waves through non-radiative relaxation. Although current endogenous and exogenous photoacoustic contrast agents effectively enable molecular imaging within deep tissues, their broad absorption spectra in the visible to near-infrared (NIR) range limit photoacoustic multiplexed imaging. Here, we exploit the distinct ultrasharp NIR absorption peaks of lanthanides to engineer a series of NIR photoacoustic nanocrystals. This engineering involves precise host and dopant material composition, yielding nanocrystals with sharply peaked photoacoustic absorption spectra (~3.2 nm width) and a ~10-fold enhancement in NIR optical absorption for efficient deep tissue imaging. By combining photoacoustic tomography with these engineered nanocrystals, we demonstrate photoacoustic multiplexed differential imaging with substantially decreased background signals and enhanced precision and contrast.Item Audio misinformation encoding via an on-phone sub-terahertz metasurface(Optica Publishing Group, 2024) Shaikhanov, Zhambyl; Al-Madi, Mahmoud; Chen, Hou-Tong; Chang, Chun-Chieh; Addamane, Sadhvikas; Mittleman, Daniel M.; Knightly, Edward W.We demonstrate a wireless security application to protect the weakest link in phone-to-phone communication, using a terahertz metasurface. To our knowledge, this is the first example of an eavesdropping countermeasure in which the attacker is actively misled.Item Reference-free structural variant detection in microbiomes via long-read co-assembly graphs(Oxford University Press, 2024) Curry, Kristen D; Yu, Feiqiao Brian; Vance, Summer E; Segarra, Santiago; Bhaya, Devaki; Chikhi, Rayan; Rocha, Eduardo P C; Treangen, Todd JMotivation: The study of bacterial genome dynamics is vital for understanding the mechanisms underlying microbial adaptation, growth, and their impact on host phenotype. Structural variants (SVs), genomic alterations of 50 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to the absence of clear reference genomes and the presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing all metagenomic samples in a series (time or other metric) into a single co-assembly graph. The log fold change in graph coverage between successive samples is then calculated to call SVs that are thriving or declining.Results: We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, particularly as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between successive time and temperature samples, suggesting host advantage. Our approach leverages previous work in assembly graph structural and coverage patterns to provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial gene flux.Availability and implementation: rhea is open source and available at: https://github.com/treangenlab/rhea.Item Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions(Springer Nature, 2024) Lamichhane, Bishal; Moukaddam, Nidal; Sabharwal, AshutoshMobile sensing-based depression severity assessment could complement the subjective questionnaires-based assessment currently used in practice. However, previous studies on mobile sensing for depression severity assessment were conducted on homogeneous mental health condition participants; evaluation of possible generalization across heterogeneous groups has been limited. Similarly, previous studies have not investigated the potential of free-living audio data for depression severity assessment. Audio recordings from free-living could provide rich sociability features to characterize depressive states. We conducted a study with 11 healthy individuals, 13 individuals with major depressive disorder, and eight individuals with schizoaffective disorders. Communication logs and location data from the participants’ smartphones and continuous audio recordings of free-living from a wearable audioband were obtained over a week for each participant. The depression severity prediction model trained using communication log and location data features had a root mean squared error (rmse) of 6.80. Audio-based sociability features further reduced the rmse to 6.07 (normalized rmse of 0.22). Audio-based sociability features also improved the F1 score in the five-class depression category classification model from 0.34 to 0.46. Thus, free-living audio-based sociability features complement the commonly used mobile sensing features to improve depression severity assessment. The prediction results obtained with mobile sensing-based features are better than the rmse of 9.83 (normalized rmse of 0.36) and the F1 score of 0.25 obtained with a baseline model. Additionally, the predicted depression severity had a significant correlation with reported depression severity (correlation coefficient of 0.76, $$p<$$0.001). Thus, our work shows that mobile sensing could model depression severity across participants with heterogeneous mental health conditions, potentially offering a screening tool for depressive symptoms monitoring in the broader population.Item Adaptation of sleep to daylight saving time is slower in people consuming a high-fat diet(Elsevier, 2024) McHill, Andrew W.; Sano, Akane; Barger, Laura K.; Phillips, Andrew J. K.; Czeisler, Charles A.; Klerman, Elizabeth B.Adaptation of the circadian clock to the environment is essential for optimal health, well-being, and performance. Animal models demonstrate that a high-fat diet impairs circadian adaptation to advances of the light-dark cycle; it is unknown whether this occurs in humans. Utilizing a natural experiment that occurs when humans must advance their behaviors to an earlier hour for daylight saving time (DST), we measured the influence of diet on sleep/wake timing relative to dim-light melatonin onset time. Students with a lower-fat diet rapidly altered their sleep-wake timing to match the imposed time change, whereas those with a high-fat diet were slower to adapt to the time change. Moreover, a faster shift in timing after DST was associated with higher general health, lower body mass index, and higher grade point average. These data suggest that diet may influence the speed of sleep and circadian adaptation, which could have implications for health and performance.Item Inductive biases of neural network modularity in spatial navigation(AAAS, 2024) Zhang, Ruiyi; Pitkow, Xaq; Angelaki, Dora E.The brain may have evolved a modular architecture for daily tasks, with circuits featuring functionally specialized modules that match the task structure. We hypothesize that this architecture enables better learning and generalization than architectures with less specialized modules. To test this, we trained reinforcement learning agents with various neural architectures on a naturalistic navigation task. We found that the modular agent, with an architecture that segregates computations of state representation, value, and action into specialized modules, achieved better learning and generalization. Its learned state representation combines prediction and observation, weighted by their relative uncertainty, akin to recursive Bayesian estimation. This agent’s behavior also resembles macaques’ behavior more closely. Our results shed light on the possible rationale for the brain’s modularity and suggest that artificial systems can use this insight from neuroscience to improve learning and generalization in natural tasks.Item Learning to express reward prediction error-like dopaminergic activity requires plastic representations of time(Springer Nature, 2024) Cone, Ian; Clopath, Claudia; Shouval, Harel Z.The dominant theoretical framework to account for reinforcement learning in the brain is temporal difference learning (TD) learning, whereby certain units signal reward prediction errors (RPE). The TD algorithm has been traditionally mapped onto the dopaminergic system, as firing properties of dopamine neurons can resemble RPEs. However, certain predictions of TD learning are inconsistent with experimental results, and previous implementations of the algorithm have made unscalable assumptions regarding stimulus-specific fixed temporal bases. We propose an alternate framework to describe dopamine signaling in the brain, FLEX (Flexibly Learned Errors in Expected Reward). In FLEX, dopamine release is similar, but not identical to RPE, leading to predictions that contrast to those of TD. While FLEX itself is a general theoretical framework, we describe a specific, biophysically plausible implementation, the results of which are consistent with a preponderance of both existing and reanalyzed experimental data.Item Inefficient tissue immune response against MPXV in an immunocompromised mpox patient(Wiley, 2024) Matschke, Jakob; Hartmann, Kristin; Pfefferle, Susanne; Wang, Yue; Valdes, Pablo A.; Thies, Edda; Schweizer, Michaela; Lütgehetmann, Marc; Schmiedel, Stefan; Bernreuther, Christian; Boyden, Edward S.; Glatzel, Markus; Krasemann, SusanneThe recent outbreak of monkeypox virus (MPXV) was unprecedented in its size and distribution. Those living with uncontrolled HIV and low CD4 T cell counts might develop a fulminant clinical mpox course with increased mortality, secondary infections, and necrotizing lesions. Fatal cases display a high and widespread MPXV tissue burden. The underlying pathomechanisms are not fully understood. We report here the pathological findings of an MPXV-driven abscess in gastrocnemius muscle requiring surgery in an immunocompromised patient with severe mpox. Presence of virus particles and infectivity were confirmed by electron microscopy, expansion microscopy, and virus culture, respectively. MPXV tissue distribution by immunohistochemistry (IHC) showed a necrotic core with infection of different cell types. In contrast, at the lesion rim fibroblasts were mainly infected. Immune cells were almost absent in the necrotic core, but were abundant at the infection rim and predominantly macrophages. Further, we detected high amounts of alternatively activated GPNMB+-macrophages at the lesion border. Of note, macrophages only rarely colocalized with virus-infected cells. Insufficient clearance of infected cells and infection of lesion-associated fibroblasts sustained by the abundance of profibrotic macrophages might lead to the coalescing of lesions and the severe and persistent clinical mpox course observed in immunocompromised patients.Item Diverse task-driven modeling of macaque V4 reveals functional specialization towards semantic tasks(Public Library of Science, 2024) Cadena, Santiago A.; Willeke, Konstantin F.; Restivo, Kelli; Denfield, George; Sinz, Fabian H.; Bethge, Matthias; Tolias, Andreas S.; Ecker, Alexander S.Responses to natural stimuli in area V4—a mid-level area of the visual ventral stream—are well predicted by features from convolutional neural networks (CNNs) trained on image classification. This result has been taken as evidence for the functional role of V4 in object classification. However, we currently do not know if and to what extent V4 plays a role in solving other computational objectives. Here, we investigated normative accounts of V4 (and V1 for comparison) by predicting macaque single-neuron responses to natural images from the representations extracted by 23 CNNs trained on different computer vision tasks including semantic, geometric, 2D, and 3D types of tasks. We found that V4 was best predicted by semantic classification features and exhibited high task selectivity, while the choice of task was less consequential to V1 performance. Consistent with traditional characterizations of V4 function that show its high-dimensional tuning to various 2D and 3D stimulus directions, we found that diverse non-semantic tasks explained aspects of V4 function that are not captured by individual semantic tasks. Nevertheless, jointly considering the features of a pair of semantic classification tasks was sufficient to yield one of our top V4 models, solidifying V4’s main functional role in semantic processing and suggesting that V4’s selectivity to 2D or 3D stimulus properties found by electrophysiologists can result from semantic functional goals.Item Cavity-coupled telecom atomic source in silicon(Springer Nature, 2024) Johnston, Adam; Felix-Rendon, Ulises; Wong, Yu-En; Chen, Songtao; Smalley-Curl InstituteNovel T centers in silicon hold great promise for quantum networking applications due to their telecom band optical transitions and the long-lived ground state electronic spins. An open challenge for advancing the T center platform is to enhance its weak and slow zero phonon line (ZPL) emission. In this work, by integrating single T centers with a low-loss, small mode-volume silicon photonic crystal cavity, we demonstrate an enhancement of the fluorescence decay rate by a factor of F = 6.89. Efficient photon extraction enables the system to achieve an average ZPL photon outcoupling rate of 73.3 kHz under saturation, which is about two orders of magnitude larger than the previously reported value. The dynamics of the coupled system is well modeled by solving the Lindblad master equation. These results represent a significant step towards building efficient T center spin-photon interfaces for quantum information processing and networking applications.Item Beta activity in human anterior cingulate cortex mediates reward biases(Springer Nature, 2024) Xiao, Jiayang; Adkinson, Joshua A.; Myers, John; Allawala, Anusha B.; Mathura, Raissa K.; Pirtle, Victoria; Najera, Ricardo; Provenza, Nicole R.; Bartoli, Eleonora; Watrous, Andrew J.; Oswalt, Denise; Gadot, Ron; Anand, Adrish; Shofty, Ben; Mathew, Sanjay J.; Goodman, Wayne K.; Pouratian, Nader; Pitkow, Xaq; Bijanki, Kelly R.; Hayden, Benjamin; Sheth, Sameer A.The rewards that we get from our choices and actions can have a major influence on our future behavior. Understanding how reward biasing of behavior is implemented in the brain is important for many reasons, including the fact that diminution in reward biasing is a hallmark of clinical depression. We hypothesized that reward biasing is mediated by the anterior cingulate cortex (ACC), a cortical hub region associated with the integration of reward and executive control and with the etiology of depression. To test this hypothesis, we recorded neural activity during a biased judgment task in patients undergoing intracranial monitoring for either epilepsy or major depressive disorder. We found that beta (12–30 Hz) oscillations in the ACC predicted both associated reward and the size of the choice bias, and also tracked reward receipt, thereby predicting bias on future trials. We found reduced magnitude of bias in depressed patients, in whom the beta-specific effects were correspondingly reduced. Our findings suggest that ACC beta oscillations may orchestrate the learning of reward information to guide adaptive choice, and, more broadly, suggest a potential biomarker for anhedonia and point to future development of interventions to enhance reward impact for therapeutic benefit.Item Population coding of strategic variables during foraging in freely moving macaques(Springer Nature, 2024) Shahidi, Neda; Franch, Melissa; Parajuli, Arun; Schrater, Paul; Wright, Anthony; Pitkow, Xaq; Dragoi, ValentinUntil now, it has been difficult to examine the neural bases of foraging in naturalistic environments because previous approaches have relied on restrained animals performing trial-based foraging tasks. Here we allowed unrestrained monkeys to freely interact with concurrent reward options while we wirelessly recorded population activity in the dorsolateral prefrontal cortex. The animals decided when and where to forage based on whether their prediction of reward was fulfilled or violated. This prediction was not solely based on a history of reward delivery, but also on the understanding that waiting longer improves the chance of reward. The task variables were continuously represented in a subspace of the high-dimensional population activity, and this compressed representation predicted the animal’s subsequent choices better than the true task variables and as well as the raw neural activity. Our results indicate that monkeys’ foraging strategies are based on a cortical model of reward dynamics as animals freely explore their environment.