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1. Dataset of polarimetric images of mechanically generated water surface waves coupled with surface elevation records by wave gauges linear array

Author

Ginio, Noam, Lindenbaum, Michael, Fishbain, Barak, and Liberzon, Dan

Subjects
Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning
Abstract

Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are essential for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limited wavenumber/frequency response. To address these challenges a novel method was developed, using polarization filter equipped camera as the main sensor and Machine Learning (ML) algorithms for data processing [1,2]. The developed method training and evaluation was based on in-house made supervised dataset. Here we present this supervised dataset of polarimetric images of the water surface coupled with the water surface elevation measurements made by a linear array of resistance-type wave gauges (WG). The water waves were mechanically generated in a laboratory waves basin, and the polarimetric images were captured under an artificial light source. Meticulous camera and WGs calibration and instruments synchronization supported high spatio-temporal resolution. The data set covers several wavefield conditions, from simple monochromatic wave trains of various steepness, to irregular wavefield of JONSWAP prescribed spectral shape and several wave breaking scenarios. The dataset contains measurements repeated in several camera positions relative to the wave field propagation direction., Comment: 15 pages, 7 figures, 3 tables. Data article. Under review in "Data in Brief" journal. The data in available for download on ScienceDB repository. arXiv admin note: substantial text overlap with arXiv:2410.14988

Published
2024

2. Wave (from) Polarized Light Learning (WPLL) method: high resolution spatio-temporal measurements of water surface waves in laboratory setups

Author

Ginio, Noam, Lindenbaum, Michael, Fishbain, Barak, and Liberzon, Dan

Subjects
Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning
Abstract

Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are crucial for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limitations in wavenumber/frequency response. To address these challenges, we propose Wave (from) Polarized Light Learning (WPLL), a learning based remote sensing method for laboratory implementation, capable of inferring surface elevation and slope maps in high resolution. The method uses the polarization properties of light reflected from the water surface. The WPLL uses a deep neural network (DNN) model that approximates the water surface slopes from the polarized light intensities. Once trained on simple monochromatic wave trains, the WPLL is capable of producing high-resolution and accurate 2D reconstruction of the water surface slopes and elevation in a variety of irregular wave fields. The method's robustness is demonstrated by showcasing its high wavenumber/frequency response, its ability to reconstruct wave fields propagating at arbitrary angles relative to the camera optical axis, and its computational efficiency. This developed methodology is an accurate and cost-effective near-real time remote sensing tool for laboratory water surface waves measurements, setting the path for upscaling to open sea application for research, monitoring, and short-time forecasting., Comment: 20 pages, 17 figures, 5 tables, under review in Applied Ocean Research Journal

Published
2024

3. Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.

Author

Wani, Agaz, Katrinli, Seyma, Zhao, Xiang, Daskalakis, Nikolaos, Zannas, Anthony, Aiello, Allison, Baker, Dewleen, Boks, Marco, Brick, Leslie, Chen, Chia-Yen, Dalvie, Shareefa, Fortier, Catherine, Geuze, Elbert, Hayes, Jasmeet, Kessler, Ronald, King, Anthony, Koen, Nastassja, Liberzon, Israel, Lori, Adriana, Luykx, Jurjen, Maihofer, Adam, Milberg, William, Miller, Mark, Mufford, Mary, Nugent, Nicole, Rauch, Sheila, Ressler, Kerry, Risbrough, Victoria, Rutten, Bart, Stein, Dan, Stein, Murray, Ursano, Robert, Verfaellie, Mieke, Vermetten, Eric, Vinkers, Christiaan, Ware, Erin, Wildman, Derek, Wolf, Erika, Nievergelt, Caroline, Logue, Mark, Smith, Alicia, and Uddin, Monica

Subjects
DNA methylation, Machine learning, PTSD, Risk scores, Humans, Stress Disorders, Post-Traumatic, DNA Methylation, Male, Military Personnel, Female, Adult, Cohort Studies, Risk Factors, Risk Assessment, Middle Aged, Machine Learning
Abstract

BACKGROUND: Incorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. METHODS: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. RESULTS: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p=0.006), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p

Published
2024

4. Smaller total and subregional cerebellar volumes in posttraumatic stress disorder: a mega-analysis by the ENIGMA-PGC PTSD workgroup

Author

Huggins, Ashley A, Baird, C Lexi, Briggs, Melvin, Laskowitz, Sarah, Hussain, Ahmed, Fouda, Samar, Haswell, Courtney, Sun, Delin, Salminen, Lauren E, Jahanshad, Neda, Thomopoulos, Sophia I, Veltman, Dick J, Frijling, Jessie L, Olff, Miranda, van Zuiden, Mirjam, Koch, Saskia BJ, Nawjin, Laura, Wang, Li, Zhu, Ye, Li, Gen, Stein, Dan J, Ipser, Jonathan, Seedat, Soraya, du Plessis, Stefan, van den Heuvel, Leigh L, Suarez-Jimenez, Benjamin, Zhu, Xi, Kim, Yoojean, He, Xiaofu, Zilcha-Mano, Sigal, Lazarov, Amit, Neria, Yuval, Stevens, Jennifer S, Ressler, Kerry J, Jovanovic, Tanja, van Rooij, Sanne JH, Fani, Negar, Hudson, Anna R, Mueller, Sven C, Sierk, Anika, Manthey, Antje, Walter, Henrik, Daniels, Judith K, Schmahl, Christian, Herzog, Julia I, Říha, Pavel, Rektor, Ivan, Lebois, Lauren AM, Kaufman, Milissa L, Olson, Elizabeth A, Baker, Justin T, Rosso, Isabelle M, King, Anthony P, Liberzon, Isreal, Angstadt, Mike, Davenport, Nicholas D, Sponheim, Scott R, Disner, Seth G, Straube, Thomas, Hofmann, David, Qi, Rongfeng, Lu, Guang Ming, Baugh, Lee A, Forster, Gina L, Simons, Raluca M, Simons, Jeffrey S, Magnotta, Vincent A, Fercho, Kelene A, Maron-Katz, Adi, Etkin, Amit, Cotton, Andrew S, O’Leary, Erin N, Xie, Hong, Wang, Xin, Quidé, Yann, El-Hage, Wissam, Lissek, Shmuel, Berg, Hannah, Bruce, Steven, Cisler, Josh, Ross, Marisa, Herringa, Ryan J, Grupe, Daniel W, Nitschke, Jack B, Davidson, Richard J, Larson, Christine L, deRoon-Cassini, Terri A, Tomas, Carissa W, Fitzgerald, Jacklynn M, Blackford, Jennifer Urbano, Olatunji, Bunmi O, Kremen, William S, Lyons, Michael J, Franz, Carol E, Gordon, Evan M, May, Geoffrey, Nelson, Steven M, Abdallah, Chadi G, Levy, Ifat, and Harpaz-Rotem, Ilan

Subjects
Clinical and Health Psychology, Psychology, Post-Traumatic Stress Disorder (PTSD), Mental Illness, Behavioral and Social Science, Anxiety Disorders, Mental Health, Brain Disorders, Mind and Body, Neurosciences, Mental health, Humans, Stress Disorders, Post-Traumatic, Cerebellum, Female, Male, Adult, Magnetic Resonance Imaging, Middle Aged, White Matter, Gray Matter, Organ Size, Deep Learning, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p-FDR

Published
2024

5. Piezoresistive snap-through detection for bifurcation-based MEMS sensors

Author

Litvinov, Ivan, Milo, Gal Spaer, Liberzon, Alexander, and Krylov, Slava

Subjects
Physics - Applied Physics
Abstract

We report on the piezoresistive method for detecting stability loss events in microelectromechanical (MEMS) sensors based on bifurcation. The method involves measuring the resistivity changes of an entire beam to detect snap-through transitions in an electrostatically actuated, bistable double-clamped crystalline Silicon (Si) microbeam. The applicability of the suggested approach in two types of sensors - an ambient air temperature sensor or a mean air velocity sensor, is demonstrated. In both cases the bistable beam, serving as the sensing element, is affected by the electrothermal Joule's heating and air cooling. The measured signal is obtained by monitoring the critical voltages of the snap-through transitions. Piezoresistive sensing is especially suitable for the response monitoring of the exposed to the environment, free-standing heated microbeam sensors, where optical, piezoelectric, or electrostatic interrogation methods are not applicable. The approach can be implemented in various bifurcation microsensors and for response monitoring of bistable actuators., Comment: 5 pages with 3 figures

Published
2023
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6. Effect of Overheat and Direct Flow Loading on the MEMS Bistable Flow Sensor

Author

Litvinov, Ivan, Refaeli, Dan, Liberzon, Alex, and Krylov, Slava

Subjects
Physics - Applied Physics
Abstract

We present the findings from an experimental study of a MEMS flow sensor in which an initially curved, double-clamped bistable microbeam is the primary sensing element. Our research explores how the overheat ratio, direct flow loading, and turbulence-induced vibration affect the sequential snap-through (ST) buckling and snap-back (SB) release of an electrostatically actuated beam heated by an electric current. The sensor is fabricated from highly doped single-crystal silicon using a silicon-on-insulator (SOI) wafer. Positioned at the chip's edge, the microbeam is exposed to airflow, enabling concurrent dynamic response measurements with a laser Doppler vibrometer and a video camera. Our research demonstrates that the overheat ratio can be significantly lower for this sensing principle than conventional thermal sensing elements, pointing to the potential for substantial energy savings. We also emphasize the significant impact of flow angles and vibrations on the critical ST and SB voltages, which are vital for the flow sensor's output. Additionally, we introduce the first direct experimental observation of the beam profile's time history during the snap-through /snap-back transition. The potential impact of this research lies in developing more robust MEMS flow sensors with enhanced sensitivity and a better understanding of their response to environmental factors, which could have broader applications in fields such as aerospace, environmental monitoring, and industrial process control., Comment: 38 pages, 11 figures

Published
2023
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7. Exploration-Exploitation Model of Moth-Inspired Olfactory Navigation

Author

Lazebnik, Teddy, Golov, Yiftach, Gurka, Roi, Harari, Ally, and Liberzon, Alex

Subjects
Computer Science - Artificial Intelligence, Computer Science - Information Retrieval, Computer Science - Robotics, Quantitative Biology - Neurons and Cognition
Abstract

Navigation of male moths toward females during the mating search offers a unique perspective on the exploration-exploitation (EE) model in decision-making. This study uses the EE model to explain male moth pheromone-driven flight paths. We leverage wind tunnel measurements and 3D tracking using infrared cameras to gain insights into male moth behavior. During the experiments in the wind tunnel, we add disturbance to the airflow and analyze the effect of increased fluctuations on moth flights in the context of the proposed EE model. We separate the exploration and exploitation phases by applying a genetic algorithm to the dataset of moth 3D trajectories. First, we demonstrate that the exploration-to-exploitation rate (EER) increases with distance from the source of the female pheromone, which can be explained in the context of the EE model. Furthermore, our findings reveal a compelling relationship between EER and increased flow fluctuations near the pheromone source. Using the open-source pheromone plume simulation and our moth-inspired navigation model, we explain why male moths exhibit an enhanced EER as turbulence levels increase, emphasizing the agent's adaptation to dynamically changing environments. This research extends our understanding of optimal navigation strategies based on general biological EE models and supports the development of advanced, theoretically supported bio-inspired navigation algorithms. We provide important insights into the potential of bio-inspired navigation models for addressing complex decision-making challenges.

Published
2023

9. Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium.

Author

Zhu, Xi, Kim, Yoojean, Ravid, Orren, He, Xiaofu, Suarez-Jimenez, Benjamin, Zilcha-Mano, Sigal, Lazarov, Amit, Lee, Seonjoo, Abdallah, Chadi, Angstadt, Michael, Averill, Christopher, Baird, C, Baugh, Lee, Blackford, Jennifer, Bomyea, Jessica, Bruce, Steven, Bryant, Richard, Cao, Zhihong, Choi, Kyle, Cisler, Josh, Cotton, Andrew, Daniels, Judith, Davenport, Nicholas, Davidson, Richard, DeBellis, Michael, Dennis, Emily, Densmore, Maria, deRoon-Cassini, Terri, Disner, Seth, Hage, Wissam, Etkin, Amit, Fani, Negar, Fercho, Kelene, Fitzgerald, Jacklynn, Forster, Gina, Frijling, Jessie, Geuze, Elbert, Gonenc, Atilla, Gordon, Evan, Gruber, Staci, Grupe, Daniel, Guenette, Jeffrey, Haswell, Courtney, Herringa, Ryan, Herzog, Julia, Hofmann, David, Hosseini, Bobak, Hudson, Anna, Huggins, Ashley, Ipser, Jonathan, Jahanshad, Neda, Jia-Richards, Meilin, Jovanovic, Tanja, Kaufman, Milissa, Kennis, Mitzy, King, Anthony, Kinzel, Philipp, Koch, Saskia, Koerte, Inga, Koopowitz, Sheri, Korgaonkar, Mayuresh, Krystal, John, Lanius, Ruth, Larson, Christine, Lebois, Lauren, Li, Gen, Liberzon, Israel, Lu, Guang, Luo, Yifeng, Magnotta, Vincent, Manthey, Antje, Maron-Katz, Adi, May, Geoffery, McLaughlin, Katie, Mueller, Sven, Nawijn, Laura, Nelson, Steven, Neufeld, Richard, Nitschke, Jack, OLeary, Erin, Olatunji, Bunmi, Olff, Miranda, Peverill, Matthew, Phan, K, Qi, Rongfeng, Quidé, Yann, Rektor, Ivan, Ressler, Kerry, Riha, Pavel, Ross, Marisa, Rosso, Isabelle, Salminen, Lauren, Sambrook, Kelly, Schmahl, Christian, Shenton, Martha, Sheridan, Margaret, Shih, Chiahao, Sicorello, Maurizio, Sierk, Anika, and van Rooij, Sanne

Subjects
Classification, Deep learning, Machine learning, Multimodal MRI, Posttraumatic stress disorder, Humans, Stress Disorders, Post-Traumatic, Reproducibility of Results, Big Data, Neuroimaging, Magnetic Resonance Imaging, Brain
Abstract

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

Published
2023

10. Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts

Author

Agaz H. Wani, Seyma Katrinli, Xiang Zhao, Nikolaos P. Daskalakis, Anthony S. Zannas, Allison E. Aiello, Dewleen G. Baker, Marco P. Boks, Leslie A. Brick, Chia-Yen Chen, Shareefa Dalvie, Catherine Fortier, Elbert Geuze, Jasmeet P. Hayes, Ronald C. Kessler, Anthony P. King, Nastassja Koen, Israel Liberzon, Adriana Lori, Jurjen J. Luykx, Adam X. Maihofer, William Milberg, Mark W. Miller, Mary S. Mufford, Nicole R. Nugent, Sheila Rauch, Kerry J. Ressler, Victoria B. Risbrough, Bart P. F. Rutten, Dan J. Stein, Murray B. Stein, Robert J. Ursano, Mieke H. Verfaellie, Eric Vermetten, Christiaan H. Vinkers, Erin B. Ware, Derek E. Wildman, Erika J. Wolf, Caroline M. Nievergelt, Mark W. Logue, Alicia K. Smith, and Monica Uddin

Subjects
DNA methylation, Machine learning, PTSD, Risk scores, Internal medicine, RC31-1245, Genetics, QH426-470
Abstract

Abstract Background Incorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. Methods Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. Results The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p=0.006), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p

Published
2024
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11. Laboratory investigation of nominally two-dimensional anabatic flow on symmetric double slopes

Author

Goldshmid, Roni H. and Liberzon, Dan

Subjects
Physics - Fluid Dynamics, Physics - Atmospheric and Oceanic Physics
Abstract

We investigated the dynamics of highly turbulent thermally driven anabatic (upslope) flow on a physical model inside a large water tank using particle image velocimetry (PIV) and a thermocouple grid. The results showed that the flow exhibited pronounced variations in velocity and temperature and, importantly, could not be accurately modeled as a two-dimensional quasi-steady flow. Five significant findings are presented to underscore the three-dimensional nature of the flow. Namely, the B-shaped mean velocity profiles, B-shaped turbulent flux profiles, synthetic streaks that revealed particles flowing perpendicular to the laser sheet, average vorticity maps revealing helical structure splitting, and identified vortices shooting away from the boundary towards the apex plume. Collectively, these findings offer novel insights into the flow behavior patterns of thermally driven complex terrain flows, which influence local weather and microclimates and are responsible for scalar transport, e.g., pollution.

Published
2023

12. Wave-by-wave forecasts in directional seas using nonlinear dispersion corrections

Author

Meisner, Eytan, Galvagno, Mariano, Andrade, David, Liberzon, Dan, and Stuhlmeier, Raphael

Subjects
Physics - Atmospheric and Oceanic Physics, Physics - Applied Physics, Physics - Fluid Dynamics
Abstract

We develop a new methodology for the deterministic forecasting of directional ocean surface waves, based on nonlinear frequency corrections. These frequency corrections can be pre-computed based on measured energy density spectra, and therefore come at no additional computational cost compared to linear theory. The nonlinear forecasting methodology is tested on highly-nonlinear, synthetically generated seas with a variety of values of average steepness and directional spreading, and shown to consistently outperform a linear forecast., Comment: 31 pages, 8 figures, 5 tables

Published
2023
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13. On local isotropy and scale dependence of pair dispersion in turbulent canopy flows

Author

Shnapp, Ron, Liberzon, Alex, Bohbot-Raviv, Yardena, and Fattal, Eyal

Subjects
Physics - Fluid Dynamics
Abstract

Canopy flows in the atmospheric surface layer play important economic and ecological roles, governing the dispersion of passive scalars in the environment. The interaction of high-velocity fluid and large-scale surface-mounted obstacles in canopy flows produces drag and causes intense, inhomogeneous, and anisotropic turbulence. In this work, we focus on the turbulent dispersion of passive scalars by studying the ``pair dispersion'' - a statistical measure of relative motion between particles. We analyze the results of a 3D-PTV experiment in a wind tunnel canopy flow, focusing on small scales. We confirm the existence of local isotropy of pair dispersion at scales smaller than a characteristic shear length scale $L_\Gamma=(\epsilon/\Gamma^3)^{1/2}$, where $\epsilon$ and $\Gamma$ are the mean dissipation rate and shear rate, respectively. Furthermore, we show that pair dispersion in this locally isotropic regime is a scale-dependent super-diffusive process, similar to what occurs in homogeneous isotropic turbulent flows. In addition, we measure the pair relative velocity correlation function, showing that its de-correlation occurs in the locally isotropic regime, and discuss the implications of this observation for modeling pair dispersion. Thus, our study extends the fundamental understanding of turbulent pair dispersion to the anisotropic, inhomogeneous, turbulent canopy flow, bringing valuable information for modeling scalar dispersion in the atmospheric surface layer., Comment: 23 pages, 9 figures

Published
2023

14. Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder

Author

Nievergelt, Caroline M., Maihofer, Adam X., Atkinson, Elizabeth G., Chen, Chia-Yen, Choi, Karmel W., Coleman, Jonathan R. I., Daskalakis, Nikolaos P., Duncan, Laramie E., Polimanti, Renato, Aaronson, Cindy, Amstadter, Ananda B., Andersen, Soren B., Andreassen, Ole A., Arbisi, Paul A., Ashley-Koch, Allison E., Austin, S. Bryn, Avdibegoviç, Esmina, Babić, Dragan, Bacanu, Silviu-Alin, Baker, Dewleen G., Batzler, Anthony, Beckham, Jean C., Belangero, Sintia, Benjet, Corina, Bergner, Carisa, Bierer, Linda M., Biernacka, Joanna M., Bierut, Laura J., Bisson, Jonathan I., Boks, Marco P., Bolger, Elizabeth A., Brandolino, Amber, Breen, Gerome, Bressan, Rodrigo Affonseca, Bryant, Richard A., Bustamante, Angela C., Bybjerg-Grauholm, Jonas, Bækvad-Hansen, Marie, Børglum, Anders D., Børte, Sigrid, Cahn, Leah, Calabrese, Joseph R., Caldas-de-Almeida, Jose Miguel, Chatzinakos, Chris, Cheema, Sheraz, Clouston, Sean A. P., Colodro-Conde, Lucía, Coombes, Brandon J., Cruz-Fuentes, Carlos S., Dale, Anders M., Dalvie, Shareefa, Davis, Lea K., Deckert, Jürgen, Delahanty, Douglas L., Dennis, Michelle F., Desarnaud, Frank, DiPietro, Christopher P., Disner, Seth G., Docherty, Anna R., Domschke, Katharina, Dyb, Grete, Kulenović, Alma Džubur, Edenberg, Howard J., Evans, Alexandra, Fabbri, Chiara, Fani, Negar, Farrer, Lindsay A., Feder, Adriana, Feeny, Norah C., Flory, Janine D., Forbes, David, Franz, Carol E., Galea, Sandro, Garrett, Melanie E., Gelaye, Bizu, Gelernter, Joel, Geuze, Elbert, Gillespie, Charles F., Goleva, Slavina B., Gordon, Scott D., Goçi, Aferdita, Grasser, Lana Ruvolo, Guindalini, Camila, Haas, Magali, Hagenaars, Saskia, Hauser, Michael A., Heath, Andrew C., Hemmings, Sian M. J., Hesselbrock, Victor, Hickie, Ian B., Hogan, Kelleigh, Hougaard, David Michael, Huang, Hailiang, Huckins, Laura M., Hveem, Kristian, Jakovljević, Miro, Javanbakht, Arash, Jenkins, Gregory D., Johnson, Jessica, Jones, Ian, Jovanovic, Tanja, Karstoft, Karen-Inge, Kaufman, Milissa L., Kennedy, James L., Kessler, Ronald C., Khan, Alaptagin, Kimbrel, Nathan A., King, Anthony P., Koen, Nastassja, Kotov, Roman, Kranzler, Henry R., Krebs, Kristi, Kremen, William S., Kuan, Pei-Fen, Lawford, Bruce R., Lebois, Lauren A. M., Lehto, Kelli, Levey, Daniel F., Lewis, Catrin, Liberzon, Israel, Linnstaedt, Sarah D., Logue, Mark W., Lori, Adriana, Lu, Yi, Luft, Benjamin J., Lupton, Michelle K., Luykx, Jurjen J., Makotkine, Iouri, Maples-Keller, Jessica L., Marchese, Shelby, Marmar, Charles, Martin, Nicholas G., Martínez-Levy, Gabriela A., McAloney, Kerrie, McFarlane, Alexander, McLaughlin, Katie A., McLean, Samuel A., Medland, Sarah E., Mehta, Divya, Meyers, Jacquelyn, Michopoulos, Vasiliki, Mikita, Elizabeth A., Milani, Lili, Milberg, William, Miller, Mark W., Morey, Rajendra A., Morris, Charles Phillip, Mors, Ole, Mortensen, Preben Bo, Mufford, Mary S., Nelson, Elliot C., Nordentoft, Merete, Norman, Sonya B., Nugent, Nicole R., O’Donnell, Meaghan, Orcutt, Holly K., Pan, Pedro M., Panizzon, Matthew S., Pathak, Gita A., Peters, Edward S., Peterson, Alan L., Peverill, Matthew, Pietrzak, Robert H., Polusny, Melissa A., Porjesz, Bernice, Powers, Abigail, Qin, Xue-Jun, Ratanatharathorn, Andrew, Risbrough, Victoria B., Roberts, Andrea L., Rothbaum, Alex O., Rothbaum, Barbara O., Roy-Byrne, Peter, Ruggiero, Kenneth J., Rung, Ariane, Runz, Heiko, Rutten, Bart P. F., de Viteri, Stacey Saenz, Salum, Giovanni Abrahão, Sampson, Laura, Sanchez, Sixto E., Santoro, Marcos, Seah, Carina, Seedat, Soraya, Seng, Julia S., Shabalin, Andrey, Sheerin, Christina M., Silove, Derrick, Smith, Alicia K., Smoller, Jordan W., Sponheim, Scott R., Stein, Dan J., Stensland, Synne, Stevens, Jennifer S., Sumner, Jennifer A., Teicher, Martin H., Thompson, Wesley K., Tiwari, Arun K., Trapido, Edward, Uddin, Monica, Ursano, Robert J., Valdimarsdóttir, Unnur, Van Hooff, Miranda, Vermetten, Eric, Vinkers, Christiaan H., Voisey, Joanne, Wang, Yunpeng, Wang, Zhewu, Waszczuk, Monika, Weber, Heike, Wendt, Frank R., Werge, Thomas, Williams, Michelle A., Williamson, Douglas E., Winsvold, Bendik S., Winternitz, Sherry, Wolf, Christiane, Wolf, Erika J., Xia, Yan, Xiong, Ying, Yehuda, Rachel, Young, Keith A., Young, Ross McD, Zai, Clement C., Zai, Gwyneth C., Zervas, Mark, Zhao, Hongyu, Zoellner, Lori A., Zwart, John-Anker, deRoon-Cassini, Terri, van Rooij, Sanne J. H., van den Heuvel, Leigh L., Stein, Murray B., Ressler, Kerry J., and Koenen, Karestan C.

Published
2024
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15. Commutation relations and stability of switched systems: a personal history

Author

Liberzon, Daniel

Subjects
Mathematics - Optimization and Control, Mathematics - Dynamical Systems
Abstract

This expository article presents an overview of research, conducted mostly between the mid-1990s and late 2000s, that explores a link between commutation relations among a family of asymptotically stable vector fields and stability properties of the switched system that these vector fields generate. This topic is viewed through the lens of the author's own involvement with it, by interspersing explanations of technical developments with personal reminiscences and anecdotes.

Published
2023

16. Improved Prediction of Settling Behaviour of Solid Particles through Machine Learning Analysis of Experimental Retention Time Data

Author

Keren, Liron Simon, Lazebnik, Teddy, and Liberzon, Alex

Subjects
Physics - Fluid Dynamics
Abstract

The motion of particles through density-stratified interfaces is a common phenomenon in environmental and engineering applications. However, the mechanics of particle-stratification interactions in various combinations of particle and fluid properties are not well understood. This study presents a novel machine-learning (ML) approach to experimental data of inertial particles crossing a density-stratified interface. A simplified particle settling experiment was conducted to obtain a large number of particles and expand the parameter range, resulting in an unprecedented data set that has been shared as open data. Using ML, the study explores new correlations that collapse the data from this, and previous work Verso et al. (2019). The ``delay time,'' which is the time between the particle exiting the interfacial layer and reaching a steady-state velocity, is found to strongly depend on six dimensionless parameters formulated by ML feature selection. The data shows a correlation between the Reynolds and Froude numbers within the range of the experiments, and the best symbolic regression is based on the Froude number only. This experiment provides valuable insights into the behavior of inertial particles in stratified layers and highlights opportunities for future improvement in predicting their motion.

Published
2023
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17. Topological entropy of switched nonlinear and interconnected systems

Author

Yang, Guosong, Liberzon, Daniel, and Hespanha, João P.

Subjects
Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control, 37B40, 93C30 (Primary) 93C57 (Secondary)
Abstract

A general upper bound for topological entropy of switched nonlinear systems is constructed, using an asymptotic average of upper limits of the matrix measures of Jacobian matrices of strongly persistent individual modes, weighted by their active rates. A general lower bound is constructed as well, using a similar weighted average of lower limits of the traces of these Jacobian matrices. In a case of interconnected structure, the general upper bound is readily applied to derive upper bounds for entropy that depend only on "network-level" information. In a case of block-diagonal structure, less conservative upper and lower bounds for entropy are constructed. In each case, upper bounds for entropy that require less information about the switching signal are also derived. The upper bounds for entropy and their relations are illustrated by numerical examples of a switched Lotka-Volterra ecosystem model., Comment: 31 pages, 3 figures

Published
2023

19. A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge

Author

Keren, Liron Simon, Liberzon, Alex, and Lazebnik, Teddy

Subjects
Computer Science - Machine Learning, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Human-Computer Interaction, Computer Science - Information Retrieval
Abstract

Discovering a meaningful symbolic expression that explains experimental data is a fundamental challenge in many scientific fields. We present a novel, open-source computational framework called Scientist-Machine Equation Detector (SciMED), which integrates scientific discipline wisdom in a scientist-in-the-loop approach, with state-of-the-art symbolic regression (SR) methods. SciMED combines a wrapper selection method, that is based on a genetic algorithm, with automatic machine learning and two levels of SR methods. We test SciMED on five configurations of a settling sphere, with and without aerodynamic non-linear drag force, and with excessive noise in the measurements. We show that SciMED is sufficiently robust to discover the correct physically meaningful symbolic expressions from the data, and demonstrate how the integration of domain knowledge enhances its performance. Our results indicate better performance on these tasks than the state-of-the-art SR software packages , even in cases where no knowledge is integrated. Moreover, we demonstrate how SciMED can alert the user about possible missing features, unlike the majority of current SR systems.

Published
2022
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20. Examining the association between posttraumatic stress disorder and disruptions in cortical networks identified using data-driven methods

Author

Yang, Jin, Huggins, Ashley A., Sun, Delin, Baird, C. Lexi, Haswell, Courtney C., Frijling, Jessie L., Olff, Miranda, van Zuiden, Mirjam, Koch, Saskia B. J., Nawijn, Laura, Veltman, Dick J., Suarez-Jimenez, Benjamin, Zhu, Xi, Neria, Yuval, Hudson, Anna R., Mueller, Sven C., Baker, Justin T., Lebois, Lauren A. M., Kaufman, Milissa L., Qi, Rongfeng, Lu, Guang Ming, Říha, Pavel, Rektor, Ivan, Dennis, Emily L., Ching, Christopher R. K., Thomopoulos, Sophia I., Salminen, Lauren E., Jahanshad, Neda, Thompson, Paul M., Stein, Dan J., Koopowitz, Sheri M., Ipser, Jonathan C., Seedat, Soraya, du Plessis, Stefan, van den Heuvel, Leigh L., Wang, Li, Zhu, Ye, Li, Gen, Sierk, Anika, Manthey, Antje, Walter, Henrik, Daniels, Judith K., Schmahl, Christian, Herzog, Julia I., Liberzon, Israel, King, Anthony, Angstadt, Mike, Davenport, Nicholas D., Sponheim, Scott R., Disner, Seth G., Straube, Thomas, Hofmann, David, Grupe, Daniel W., Nitschke, Jack B., Davidson, Richard J., Larson, Christine L., deRoon-Cassini, Terri A., Blackford, Jennifer U., Olatunji, Bunmi O., Gordon, Evan M., May, Geoffrey, Nelson, Steven M., Abdallah, Chadi G., Levy, Ifat, Harpaz-Rotem, Ilan, Krystal, John H., Morey, Rajendra A., and Sotiras, Aristeidis

Published
2024
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22. Rare copy number variation in posttraumatic stress disorder

Author

Maihofer, Adam X, Engchuan, Worrawat, Huguet, Guillaume, Klein, Marieke, MacDonald, Jeffrey R, Shanta, Omar, Thiruvahindrapuram, Bhooma, Jean-louis, Martineau, Saci, Zohra, Jacquemont, Sebastien, Scherer, Stephen W, Ketema, Elizabeth, Aiello, Allison E, Amstadter, Ananda B, Avdibegović, Esmina, Babic, Dragan, Baker, Dewleen G, Bisson, Jonathan I, Boks, Marco P, Bolger, Elizabeth A, Bryant, Richard A, Bustamante, Angela C, Caldas-de-Almeida, Jose Miguel, Cardoso, Graça, Deckert, Jurgen, Delahanty, Douglas L, Domschke, Katharina, Dunlop, Boadie W, Dzubur-Kulenovic, Alma, Evans, Alexandra, Feeny, Norah C, Franz, Carol E, Gautam, Aarti, Geuze, Elbert, Goci, Aferdita, Hammamieh, Rasha, Jakovljevic, Miro, Jett, Marti, Jones, Ian, Kaufman, Milissa L, Kessler, Ronald C, King, Anthony P, Kremen, William S, Lawford, Bruce R, Lebois, Lauren AM, Lewis, Catrin, Liberzon, Israel, Linnstaedt, Sarah D, Lugonja, Bozo, Luykx, Jurjen J, Lyons, Michael J, Mavissakalian, Matig R, McLaughlin, Katie A, McLean, Samuel A, Mehta, Divya, Mellor, Rebecca, Morris, Charles Phillip, Muhie, Seid, Orcutt, Holly K, Peverill, Matthew, Ratanatharathorn, Andrew, Risbrough, Victoria B, Rizzo, Albert, Roberts, Andrea L, Rothbaum, Alex O, Rothbaum, Barbara O, Roy-Byrne, Peter, Ruggiero, Kenneth J, Rutten, Bart PF, Schijven, Dick, Seng, Julia S, Sheerin, Christina M, Sorenson, Michael A, Teicher, Martin H, Uddin, Monica, Ursano, Robert J, Vinkers, Christiaan H, Voisey, Joanne, Weber, Heike, Winternitz, Sherry, Xavier, Miguel, Yang, Ruoting, McD Young, Ross, Zoellner, Lori A, Salem, Rany M, Shaffer, Richard A, Wu, Tianying, Ressler, Kerry J, Stein, Murray B, Koenen, Karestan C, Sebat, Jonathan, and Nievergelt, Caroline M

Subjects
Post-Traumatic Stress Disorder (PTSD), Mental Health, Human Genome, Genetics, Neurosciences, Brain Disorders, Mental health, Humans, DNA Copy Number Variations, Stress Disorders, Post-Traumatic, Genome, Brain, Genome-Wide Association Study, Polymorphism, Single Nucleotide, Genetic Predisposition to Disease, Psychiatric Genomics Consortium PTSD Working Group, Psychiatric Genomics Consortium CNV Working Group, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Posttraumatic stress disorder (PTSD) is a heritable (h2 = 24-71%) psychiatric illness. Copy number variation (CNV) is a form of rare genetic variation that has been implicated in the etiology of psychiatric disorders, but no large-scale investigation of CNV in PTSD has been performed. We present an association study of CNV burden and PTSD symptoms in a sample of 114,383 participants (13,036 cases and 101,347 controls) of European ancestry. CNVs were called using two calling algorithms and intersected to a consensus set. Quality control was performed to remove strong outlier samples. CNVs were examined for association with PTSD within each cohort using linear or logistic regression analysis adjusted for population structure and CNV quality metrics, then inverse variance weighted meta-analyzed across cohorts. We examined the genome-wide total span of CNVs, enrichment of CNVs within specified gene-sets, and CNVs overlapping individual genes and implicated neurodevelopmental regions. The total distance covered by deletions crossing over known neurodevelopmental CNV regions was significant (beta = 0.029, SE = 0.005, P = 6.3 × 10-8). The genome-wide neurodevelopmental CNV burden identified explains 0.034% of the variation in PTSD symptoms. The 15q11.2 BP1-BP2 microdeletion region was significantly associated with PTSD (beta = 0.0206, SE = 0.0056, P = 0.0002). No individual significant genes interrupted by CNV were identified. 22 gene pathways related to the function of the nervous system and brain were significant in pathway analysis (FDR q

Published
2022

24. A comparison of methods to harmonize cortical thickness measurements across scanners and sites

Author

Sun, Delin, Rakesh, Gopalkumar, Haswell, Courtney C, Logue, Mark, Baird, C Lexi, O'Leary, Erin N, Cotton, Andrew S, Xie, Hong, Tamburrino, Marijo, Chen, Tian, Dennis, Emily L, Jahanshad, Neda, Salminen, Lauren E, Thomopoulos, Sophia I, Rashid, Faisal, Ching, Christopher RK, Koch, Saskia BJ, Frijling, Jessie L, Nawijn, Laura, van Zuiden, Mirjam, Zhu, Xi, Suarez-Jimenez, Benjamin, Sierk, Anika, Walter, Henrik, Manthey, Antje, Stevens, Jennifer S, Fani, Negar, van Rooij, Sanne JH, Stein, Murray, Bomyea, Jessica, Koerte, Inga K, Choi, Kyle, van der Werff, Steven JA, Vermeiren, Robert RJM, Herzog, Julia, Lebois, Lauren AM, Baker, Justin T, Olson, Elizabeth A, Straube, Thomas, Korgaonkar, Mayuresh S, Andrew, Elpiniki, Zhu, Ye, Li, Gen, Ipser, Jonathan, Hudson, Anna R, Peverill, Matthew, Sambrook, Kelly, Gordon, Evan, Baugh, Lee, Forster, Gina, Simons, Raluca M, Simons, Jeffrey S, Magnotta, Vincent, Maron-Katz, Adi, du Plessis, Stefan, Disner, Seth G, Davenport, Nicholas, Grupe, Daniel W, Nitschke, Jack B, deRoon-Cassini, Terri A, Fitzgerald, Jacklynn M, Krystal, John H, Levy, Ifat, Olff, Miranda, Veltman, Dick J, Wang, Li, Neria, Yuval, De Bellis, Michael D, Jovanovic, Tanja, Daniels, Judith K, Shenton, Martha, van de Wee, Nic JA, Schmahl, Christian, Kaufman, Milissa L, Rosso, Isabelle M, Sponheim, Scott R, Hofmann, David Bernd, Bryant, Richard A, Fercho, Kelene A, Stein, Dan J, Mueller, Sven C, Hosseini, Bobak, Phan, K Luan, McLaughlin, Katie A, Davidson, Richard J, Larson, Christine L, May, Geoffrey, Nelson, Steven M, Abdallah, Chadi G, Gomaa, Hassaan, Etkin, Amit, Seedat, Soraya, Harpaz-Rotem, Ilan, Liberzon, Israel, van Erp, Theo GM, Quidé, Yann, Wang, Xin, Thompson, Paul M, and Morey, Rajendra A

Subjects
Good Health and Well Being, Adolescent, Adult, Aged, Aged, 80 and over, Case-Control Studies, Child, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Stress Disorders, Post-Traumatic, Young Adult, Data Harmonization, Scanner Effects, Site Effects, Cortical Thickness, ComBat, ComBat-GAM, Linear Mixed-Effects Model, General Additive Model, PTSD, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants' demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2-81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3-85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.

Published
2022

26. Schlieren and BOS velocimetry of a round turbulent helium jet in air

Author

Settles, Gary S. and Liberzon, Alex

Subjects
Physics - Fluid Dynamics, Physics - Applied Physics
Abstract

Seedless velocimetry is gaining interest in many industrial and research applications. We report on a comparative study of time-resolved optical velocimetry using traditional, mirror-type knife-edge schlieren optics versus Background-Oriented Schlieren (BOS) of subsonic round turbulent helium jets in air at Red = 5,890 and 11,300. Digital images with 1024 pixels streamwise resolution (0 < x/d < 200) were captured at 6000 frames/s in large ensembles. Velocimetry was performed on these results by digital image correlation (DIC) using OpenPIV software, and by streak-schlieren analysis of x-t diagrams (kymography). Limited PIV data were also collected for verification of the schlieren velocimetry results. Both BOS and traditional schlieren show partial success in measuring the mean-flow helium jet self-similarity in terms of the 1/x decay of centerline velocity, Gaussian-shaped radial velocity profiles, and linear spreading rate of the jet. Visualized turbulent eddies, used as tracers in schlieren velocimetry, are observed to last longer than is necessary for this purpose in the present helium jets. Also, the measured convective velocity appears to be sufficiently robust to sum to the jet mean velocity in some of the results. Kymography yields better overall results than DIC, which we attribute to kymography's spatiotemporal "spectrum" of jet velocities, enabling the discrimination of fast eddies near the jet centerline from slower ones near the jet periphery. DIC and other analysis methods suffer from a path-averaging bias which negatively affects the results. The reduction of kymographic data for velocimetry was done manually and also by a Fourier-transform image-feature-orientation code, both yielding equivalent results.

Published
2022
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27. Modeling of growth of the macroalga Ulva sp. in a controlled photobioreactor based on nitrogen accumulation dynamics

Author

Meiron Zollmann, Alexander Liberzon, and Alexander Golberg

Subjects
Biorefinery feedstock, macroalgae-based bioeconomy, Nitrogen Use Efficiency, precision agriculture, production model, Ulva, Aquaculture. Fisheries. Angling, SH1-691, Environmental sciences, GE1-350
Abstract

ABSTRACTMacroalgal biomass production models that capture nutrient dynamics, temperature, light and salinity are important for the design and operation of large-scale farms. The goal of this study is to understand how the nitrogen fertilizing regime, relating to dose (µM N week−1), amplitude (µM N) and duration (hours) of fertilization, affects the dynamics of nitrogen content and biomass production of Ulva sp. We hypothesize that the nitrogen fertilizing regime controls the Ulva Nitrogen Use Efficiency (NUE), defined here as the fraction of fertilizer nitrogen that is utilized and allocated to yield N, and, accordingly, also nitrogen assimilation in the biomass and the growth rate. We test this hypothesis by measuring internal nitrogen and biomass weight and by calculating NUE under various fertilization regimes in controlled photobioreactors. Based on these experimental data, we developed a biomass productivity model that predicts nitrogen and biomass dynamics temporally over three weeks of cultivation. This study highlights efficient fertilizing regimes and enables the development of a comprehensive understanding of the dynamic relationship between external N, internal N and biomass production of Ulva sp. under varying external N levels, which is important for real-world agricultural applications. This study provides a better understanding of the external N-internal N-biomass triangle leading to an improved dynamic cultivation model, enabling better control of nutrient application and biomass production in macroalgal farming for a sustainable marine bioeconomy.

Published
2023
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31. Development of a brief bedside tool to screen women sexual assault survivors for risk of persistent posttraumatic stress six months after sexual assault

Author

Walsh, Kate, Short, Nicole, Ji, Yin Yao, An, Xin Ming, Witkemper, Kristen D., Lechner, Megan, Bell, Kathy, Black, Jenny, Buchanan, Jennie, Ho, Jeffrey, Reed, Gordon, Platt, Melissa, Riviello, Ralph, Martin, Sandra L., Liberzon, Israel, Rauch, Sheila A.M., Bollen, Kenneth, and McLean, Samuel A.

Published
2024
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34. Enhancing Discovery of Genetic Variants for Posttraumatic Stress Disorder Through Integration of Quantitative Phenotypes and Trauma Exposure Information

Author

Maihofer, Adam X, Choi, Karmel W, Coleman, Jonathan RI, Daskalakis, Nikolaos P, Denckla, Christy A, Ketema, Elizabeth, Morey, Rajendra A, Polimanti, Renato, Ratanatharathorn, Andrew, Torres, Katy, Wingo, Aliza P, Zai, Clement C, Aiello, Allison E, Almli, Lynn M, Amstadter, Ananda B, Andersen, Soren B, Andreassen, Ole A, Arbisi, Paul A, Ashley-Koch, Allison E, Austin, S Bryn, Avdibegović, Esmina, Borglum, Anders D, Babić, Dragan, Bækvad-Hansen, Marie, Baker, Dewleen G, Beckham, Jean C, Bierut, Laura J, Bisson, Jonathan I, Boks, Marco P, Bolger, Elizabeth A, Bradley, Bekh, Brashear, Meghan, Breen, Gerome, Bryant, Richard A, Bustamante, Angela C, Bybjerg-Grauholm, Jonas, Calabrese, Joseph R, Caldas-de-Almeida, José M, Chen, Chia-Yen, Dale, Anders M, Dalvie, Shareefa, Deckert, Jürgen, Delahanty, Douglas L, Dennis, Michelle F, Disner, Seth G, Domschke, Katharina, Duncan, Laramie E, Džubur Kulenović, Alma, Erbes, Christopher R, Evans, Alexandra, Farrer, Lindsay A, Feeny, Norah C, Flory, Janine D, Forbes, David, Franz, Carol E, Galea, Sandro, Garrett, Melanie E, Gautam, Aarti, Gelaye, Bizu, Gelernter, Joel, Geuze, Elbert, Gillespie, Charles F, Goçi, Aferdita, Gordon, Scott D, Guffanti, Guia, Hammamieh, Rasha, Hauser, Michael A, Heath, Andrew C, Hemmings, Sian MJ, Hougaard, David Michael, Jakovljević, Miro, Jett, Marti, Johnson, Eric Otto, Jones, Ian, Jovanovic, Tanja, Qin, Xue-Jun, Karstoft, Karen-Inge, Kaufman, Milissa L, Kessler, Ronald C, Khan, Alaptagin, Kimbrel, Nathan A, King, Anthony P, Koen, Nastassja, Kranzler, Henry R, Kremen, William S, Lawford, Bruce R, Lebois, Lauren AM, Lewis, Catrin, Liberzon, Israel, Linnstaedt, Sarah D, Logue, Mark W, Lori, Adriana, Lugonja, Božo, Luykx, Jurjen J, Lyons, Michael J, Maples-Keller, Jessica L, Marmar, Charles, Martin, Nicholas G, Maurer, Douglas, and Mavissakalian, Matig R

Subjects
Biological Sciences, Genetics, Anxiety Disorders, Mental Health, Brain Disorders, Post-Traumatic Stress Disorder (PTSD), Prevention, Human Genome, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Phenotype, Polymorphism, Single Nucleotide, Stress Disorders, Post-Traumatic, GWAS, Heritability, PTSD, PheWAS, Trauma, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological sciences, Biomedical and clinical sciences, Psychology
Abstract

BackgroundPosttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs).MethodsA GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms.ResultsGWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program.ConclusionsThrough using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods.

Published
2022

35. Next generation combined sonic-hotfilm anemometer: wind alignment and automated calibration procedure using deep learning

Author

Goldshmid, Roni H., Winiarska, Ewelina, and Liberzon, Dan

Subjects
Physics - Fluid Dynamics
Abstract

The study of naturally occurring turbulent flows requires ability to collect empirical data down to the fine scales. While hotwire anemometry offers such ability, the open field studies are uncommon due to the cumbersome calibration procedure and operational requirements of hotwire anemometry, e.g., constant ambient properties and steady flow conditions. The combo probe-the combined sonic-hotfilm anemometer developed and tested over the last decade-has demonstrated its ability to overcome this hurdle. The old-er generation had a limited wind alignment range of 120 degrees and the in-situ calibration procedure was human decision based. This study presents the next generation of the combo probe design, and the new fully automated in-situ calibration procedure implementing deep learning. The elegant new design now enables measurements of the incoming wind flow in a 360-degree range. The improved calibration procedure is shown to have the robustness necessary for operation in everchanging open field flow and environmental conditions. This is especially useful with diurnally changing environments or non-stationary measuring stations, i.e., probes placed on moving platforms like boats, drones, and weather balloons. Together, the updated design and the new calibration procedure, allow for continuous field measurements with minimal to no human interaction, enabling near real-time monitoring of fine-scale turbulent fluctuations. Integration of these probes will contribute toward generation of a large pool of field data to be collected to unravel the intricacies of all scales of turbulent flows occurring in natural setups.

Published
2021
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36. Effects of season, depth and pre-cultivation fertilizing on Ulva growth dynamics offshore the Eastern Mediterranean Sea

Author

Meiron Zollmann, Alex Liberzon, Ruslana R. Palatnik, David Zilberman, and Alexander Golberg

Subjects
Medicine, Science
Abstract

Abstract Offshore macroalgae production could provide an alternative source of biomass for food, materials and energy. However, the offshore environment in general, specifically the Eastern Mediterranean Sea (EMS) offshore, is a high energy and low nutrients environment, thus challenging for macroalgae farming. In this study, we experimentally investigated the impact of season, depth, and pre-cultivation fertilization duration on the growth rates and chemical composition of offshore Ulva biomass, and developed a predictive model tailored to offshore conditions, capable of estimating both biomass growth rate and nitrogen content. Specifically, we measured Ulva biomass growth rate and internal nitrogen in the nitrogen-poor EMS a few kilometers offshore the Israeli coast at various depths and on-shore pre-cultivation fertilization schedules. Based on these data, we constructed a predictive cultivation model of Ulva offshore growth, which allows for the optimization of fertilization requirements for offshore cultivation. This study provides new insights on the effects of seasonality, depth, and pre-cultivation fertilization duration on growth rates and chemical composition of offshore Ulva sp. biomass production.

Published
2023
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44. Morning light treatment for traumatic stress: The role of amygdala reactivity study protocol

Author

Cenkner, David P, Burgess, Helen J, Huizenga, Brooke, Duval, Elizabeth R, Kim, Hyungjin Myra, Phan, K Luan, Liberzon, Israel, Klumpp, Heide, Abelson, James, Horwitz, Adam, Mooney, Ann, Raglan, Greta B, and Zalta, Alyson K

Subjects
Clinical and Health Psychology, Psychology, Brain Disorders, Anxiety Disorders, Behavioral and Social Science, Depression, Mental Health, Clinical Research, Post-Traumatic Stress Disorder (PTSD), Neurosciences, Mind and Body, 6.6 Psychological and behavioural, Evaluation of treatments and therapeutic interventions, Mental health, Good Health and Well Being, Amygdala, Anxiety, Humans, Psychotherapy, Stress Disorders, Post-Traumatic, General Science & Technology
Abstract

BackgroundExposure to trauma can result in various mental health disorders including anxiety, depression, and posttraumatic stress disorder (PTSD). Although psychotherapies and pharmacotherapies exist for the treatment of these disorders, many individuals fail to receive treatment and among those who do, many remain symptomatic. Therefore, it is critical to continue developing new interventions for traumatic stress that target underlying mechanisms of pathology and offer a safe and acceptable alternative to current treatments. Morning light treatment has good potential as a novel non-invasive, low risk treatment for traumatic stress. Evidence suggests that morning light may improve traumatic stress by reducing reactivity in the amygdala, a brain region implicated in the pathophysiology of PTSD and anatomically linked to circadian photoreceptors in the eye.MethodsIn this study, we aim to establish a significant dose-response relationship between duration of morning light treatment and reduction in amygdala reactivity among individuals with traumatic stress symptoms (NCT# 04117347). Using a transdiagnostic approach, sixty-six individuals with a history of a DSM-5 criterion A trauma and traumatic stress symptoms will be recruited to participate in a 5-week study. Participants will be randomized across three treatment arms based on morning light treatment duration: 15-minutes, 30-minutes, or 60-minutes of light treatment per day for four weeks. To evaluate amygdala activity, participants will undergo fMRI at pre-treatment, mid-treatment, and post-treatment. Participants will also complete clinical assessments and self-report measures of PTSD, depression, and anxiety at pre-treatment, mid-treatment, and post-treatment.DiscussionMorning light therapy may be an acceptable, feasible, and effective treatment for individuals suffering from traumatic stress. Identifying mechanistically relevant targets, and the doses needed to impact them, are critical steps in developing this new treatment approach for the sequelae of traumatic stress.

Published
2022

46. Fear Extinction Learning in Posttraumatic Stress Disorder

Author

Lokshina, Yana, Sheynin, Jony, Vogt, Gregory S., Liberzon, Israel, Geyer, Mark A., Series Editor, Marsden, Charles A., Series Editor, Ellenbroek, Bart A., Series Editor, Barnes, Thomas R. E., Series Editor, Andersen, Susan L., Series Editor, Paulus, Martin P., Series Editor, Olivier, Jocelien, Series Editor, Milad, Mohammed R., editor, and Norrholm, Seth D., editor

Published
2023
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47. Risk Simulation Challenges and Success Driven Project Management

Author

Shavyrina, Victoria, Liberzon, Vladimir, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Bushuyev, Sergey, editor, Ding, Ronggui, editor, and Radujkovic, Mladen, editor

Published
2023
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48. Schedule Optimisation Methods and Tools

Author

Liberzon, Vladimir, Shavyrina, Victoria, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Bushuyev, Sergey, editor, Ding, Ronggui, editor, and Radujkovic, Mladen, editor

Published
2023
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