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Cell reports. Medicine 2025年 102151页

作者： Hao Zhu Xiaoyu Tong Nancy B Carlisle Hua Xie Corey J Keller Desmond J Oathes Feng Liu Charles B Nemeroff Gregory A Fonzo Yu Zhang Department of Bioengineering Lehigh University Bethlehem PA USA. Department of Psychology Lehigh University Bethlehem PA USA. Center for Neuroscience Research Children's National Hospital Washington DC USA. Wu Tsai Neuroscience Institute Stanford University Stanford CA USA Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford CA USA Veterans Affairs Palo Alto Healthcare System and the Sierra Pacific Mental Illness Research Education and Clinical Center (MIRECC) Palo Alto CA USA. Center for Brain Imaging and Stimulation Department of Psychiatry University of Pennsylvania Perelman School of Medicine Philadelphia PA USA Center for Neuromodulation in Depression and Stress Department of Psychiatry University of Pennsylvania Perelman School of Medicine Philadelphia PA USA Penn Brain Science Translation Innovation and Modulation Center University of Pennsylvania Perelman School of Medicine Philadelphia PA USA Departments of Neurology Neurosurgery Bioengineering and Neuroscience University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Department of Systems and Enterprises Stevens Institute of Technology Hoboken NJ USA. Center for Psychedelic Research and Therapy Department of Psychiatry and Behavioral Sciences Dell Medical School The University of Texas at Austin Austin TX USA. Department of Bioengineering Lehigh University Bethlehem PA USA Department of Electrical and Computer Engineering Lehigh University Bethlehem PA USA. Electronic address: yuzi20@lehigh.edu.

Major depressive disorder (MDD) is highly heterogeneous, posing challenges for effective treatment due to complex interactions between clinical symptoms and neurobiological features. To address this, we apply contrastive principal-component analysis to fMRI-based resting-state functional connectivity, isolating disorder-specific variations by contrasting data from 233 MDD patients and 285 healthy controls. Subsequently, we use sparse canonical correlation analysis to identify two significant dimensions linking distinct brain circuits with clinical profiles. One dimension relates to an internalizing-externalizing symptom spectrum involving visual and limbic networks and is associated with cognitive task reaction times. The other dimension, linked to personality traits protective against depression (e.g., extraversion), is driven by dorsal attention network connections and correlates with cognitive control and psychomotor performance. This approach illuminates stable symptom dimensions and their neurophysiological underpinnings, aiding in precision phenotyping for MDD and supporting the development of targeted, individualized therapeutic strategies for mental health care.

关键词： brain-symptom dimension contrastive machine learning disease heterogeneity fMRI functional connectivity major depressive disorder

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From FAIR to CURE: Guidelines for Computational Models of Biological Systems

arXiv

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arXiv 2025年

作者： Sauro, Herbert M. Agmon, Eran Blinov, Michael L. Gennari, John H. Hellerstein, Joe Heydarabadipour, Adel Hunter, Peter Jardine, Bartholomew E. May, Elebeoba Nickerson, David P. Smith, Lucian P. Bader, Gary D. Bergmann, Frank Boyle, Patrick M. Dräger, Andreas Faeder, James R. Feng, Song Freire, Juliana Fröhlich, Fabian Glazier, James A. Gorochowski, Thomas E. Helikar, Tomas Hoops, Stefan Imoukhuede, Princess Keating, Sarah M. Konig, Matthias Laubenbacher, Reinhard Loew, Leslie M. Lopez, Carlos F. Lytton, William W. McCulloch, Andrew Mendes, Pedro Myers, Chris J. Myers, Jerry G. Mulugeta, Lealem Niarakis, Anna van Niekerk, David D. Olivier, Brett G. Patrie, Alexander A. Quardokus, Ellen M. Radde, Nicole Rohwer, Johann M. Sahle, Sven Schaff, James C. Sego, T.J. Shin, Janis Snoep, Jacky L. Vadigepalli, Rajanikanth Wiley, H. Steve Waltemath, Dagmar Moraru, Ion Department of Bioengineering University of Washington SeattleWA98195-5061 United States Intelligent Systems Engineering and Biocomplexity Institute Indiana University Street BloomingtonIN47408 United States Center for Cardiovascular Biology University of Washington SeattleWA98195-5061 United States eScience Institute University of Washington SeattleWA98195-5061 United States Institute for Stem Cell and Regenerative Medicine University of Washington SeattleWA98195-5061 United States School of Biological Sciences University of Bristol 24 Tyndall Avenue BristolBS8 1TQ United Kingdom Departments of Physiology & Pharmacology Neurology Downstate Health Science University BrooklynNY11203 United States Department of Neurology Kings County Hospital BrooklynNY11203 United States NASA-John H. Glenn Research Center MS 110-3 21000 Brookpark Road ClevelandOH44135 United States Center for Cell Analysis and Modeling UConn Health 263 Farmington Avenue FarmingtonCT06030-6406 United States Amsterdam Institute for Life and Environment Vrije Universiteit Amsterdam De Boelelaan 1108 Amsterdam1081 HZ Netherlands Department of Pathology and Genomic Medicine Thomas Jefferson University 1020 Locust St PhiladelphiaPA19107 United States Biological Sciences Division Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States Auckland Bioengineering Institute University of Auckland Auckland1010 New Zealand Center of Integrative Biology University of Toulouse III-Paul Sabatier 165 Rue Marianne Grunberg-Manago Toulouse31400 France Lifeware Group Inria Building Alan Turing 1 Rue Honoré d’Estienne d’Orves Palaiseau91120 France Multiscale Modeling Group Altos Labs Redwood CityCA94065 United States partner site Tübingen Tübingen Germany Ottfried-Müller-Str. 37 Tübingen72076 Germany 06120 Germany Department of Computer Science Center for Data Science New York University New YorkNY11201 United States Department of Bi

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility;the clarity of model descriptions and annotations for understandability;adherence to standards and open science practices for reproducibility;and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible. © 2025, CC BY.

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Comparative characterization of in vivo and in vitro noise of the SIROF Utah electrode array

Comparative characterization of in vivo and in vitro noise o...

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IEEE Sensors

作者： A. Tye Gardner John Mize David J. Warren Ross M. Walker Departments of Electrical and Computer Engineering University of Utah Departments of Electrical and Computer Bioengineering University of Utah

ISBN: (纸本)9781509010134

Microelectrode arrays are one of the most important tools for advancing our knowledge of biological neural systems. Thorough characterization of microelectrode arrays helps designers optimize neural interface circuits and systems as well as microelectrodes themselves, which in turn enables a better understanding of nervous systems. Noise and impedance of electrode arrays are important for determining specifications for neural interface electronics, but data are largely lacking in the literature. This work presents engineering-oriented characterizations of the commonly used SIROF Utah Electrode Array. The study demonstrates a strong correlation between noise and impedance, and finds a significant increase in both noise and impedance in vivo versus in vitro. We also observe an increase in low-frequency (<;1 kHz) noise in vivo that is consistent with biological noise reported elsewhere.

关键词： Utah Electrode Array Neural interface Neural amplifier Electrode impedance Electrode noise In vivo noise electrode impedance Noise electrode configuration impedance Microelectrodes Utah interface electronics Biological noise interface circuit Electrodes arrays In vivo

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One resistor and two capacitors: An electrical engineer's simple view of a biological cell

One resistor and two capacitors: An electrical engineer's si...

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Xiao Ma Xiaotian Du N. Gholizadeh V. Gholizadeh Hang Li Xuanhong Cheng James C. M. Hwang Departments of Electrical and Computer Engineering Materials Science and Engineering and Bioengineering Lehigh University Bethlehem Pennsylvania USA

Based on a simple three-layer model, a network of three lumped elements of membrane capacitance, cytoplasm resistance, and cytoplasm capacitance was used to explain the so-called β dispersion in the dielectric properties of biological cells hypothesized six decades ago. Using the simple network and without resorting to frequency-dependent permittivity, nondispersive membrane capacitance, cytoplasm resistance, and cytoplasm capacitance were extracted from microwave scattering parameters measured on single cells without the complication of electrode polarization or boundary layer. The extracted values, as small as 1 pF for the membrane capacitance, as large as 0.1 M for the cytoplasm resistance, and as small as 10 fF for the cytoplasm capacitance, agreed with the hypothesized values, as well as order-of-magnitude estimates from physical properties of cells.

关键词： Dispersion Capacitance Coplanar waveguides Biomembranes Microwave measurement Electrodes Dielectric constant

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Stain-free structural and molecular histopathology using programmable supercontinuum pulses

Stain-free structural and molecular histopathology using pro...

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Novel Techniques in Microscopy, NTM 2017

作者： Boppart, Stephen A. Beckman Institute for Advanced Science and Technology Departments of Electrical and Computer Engineering Bioengineering and Medicine 405 N. Mathews Avenue UrbanaIL61801 United States University of Illinois Urbana-Champaign United States

Generation and programmable shaping of widely-coherent supercontinuum pulses enables multimodal multiphoton imaging for basic science investigations of carcinogenesis and novel biomarkers for clinical cancer diagnosti... 详细信息

ISBN: (纸本)9781943580255

关键词： Supercontinua

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Second-harmonic patterned polarization-analyzed reflection confocal microscopy of collagen

Second-harmonic patterned polarization-analyzed reflection c...

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Frontiers in Optics, FiO 2017

作者： Okoro, Chukwuemeka Toussaint, Kimani C. Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Mechanical Science and Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Affiliate in the departments of Electrical and Computer Engineering Bioengineering The Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign UrbanaIL61801 United States

Polarization analysis of images obtained by combining confocal microscopy with a second-harmonic mask permits traditional polarimetric metrics to be adapted to collagen imaging. This approach applies to any nonlinear ... 详细信息

ISBN: (纸本)9781943580330

关键词： Confocal microscopy

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Freeform assembly planning

arXiv

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arXiv 2018年

作者： Gelber, Matthew K. Hurst, Greg Bhargava, Rohit Beckman Institute for Advanced Science and Technology Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Wolfram Research ChampaignIL61820 United States Beckman Institute for Advanced Science and Technology Departments of Chemistry Chemical and Biomolecular Engineering Mechanical Science and Engineering and Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL United States

3D printing enables the fabrication of complex architectures at multiple length scales by automating large sequences of additive steps. The increasing sophistication of printers, materials, and generative design promises to make geometric complexity a non-issue in manufacturing;however, this complexity can only be realized if a design can be translated into a physically executable sequence of printing operations. We investigate this planning problem for freeform direct-write assembly, in which filaments of material are deposited through a nozzle translating along a 3D path to create sparse, frame-like structures. We enumerate the process constraints for different variants of the freeform assembly process and show that, in the case where material stiffens via a glass transition, determining whether a feasible sequence exists is NP-complete. Nonetheless, for topologies typically encountered in real-world applications, finding a feasible or even optimal sequence is a tractable problem. We develop a sequencing algorithm that maximizes the fidelity of the printed part and minimizes the probability of print failure by modeling the assembly as a linear, elastic frame. We implement the algorithm and validate our approach experimentally, printing objects composed of thousands of large-aspect-ratio sugar alcohol filaments with diameters of 100-200 μm. Note to Practitioners-Extrusion-style 3D printers typically pattern material in a series of 2D layers, but they can be also be programmed to deposit material along 3D paths in a "freeform" fashion. However, programming a printer to operate in this way requires consideration of constraints related to collision and stability. For large designs, finding an optimal or even feasible plan with respect to these constraints requires automated planning. We address a hard version of this problem in which any joint in the frame can only support one cantilever at a time. We develop an exact algorithm that maximizes the robustness of the pri

关键词： 3D printers

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Continuous glucose monitoring identifies relationship between optimized glycemic control and post-discharge acute care facility needs

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BMC Research Notes 2018年第1期11卷 1-5页

作者： Pappada, Scott M. Woodling, Karina Owais, Mohammad Hamza Zink, Evan M. Dahbour, Layth Tripathi, Ravi S. Khuder, Sadik A. Papadimos, Thomas J. Department of Anesthesiology University of Toledo College of Medicine and Life Sciences 3000 Arlington Avenue Toledo 43614 OH United States Department of Bioengineering University of Toledo College of Engineering 3000 Arlington Avenue Toledo 43614 OH United States Department of Anesthesiology Ohio State University Wexner Medical Center 410 West 10th Avenue Columbus 43210 OH United States Department of Electrical Engineering and Computer Science University of Toledo College of Engineering Toledo 43606 OH United States Department of Medicine University of Toledo College of Medicine and Life Sciences Toledo 43614 OH United States Departments of Anesthesiology and Medical Microbiology and Immunology University of Toledo College of Medicine and Life Sciences 3000 Arlington Avenue Toledo 43614 OH United States

Objective: Hyperglycemia is an independent risk factor in hospitalized patients for adverse outcomes, even if patients are not diabetic. We used continuous glucose monitoring to evaluate whether glycemic control (hyperglycemia) in the first 72 h after an intensive care admission was associated with the need for admission to a post discharge long-term medical facility. Results: We enrolled 59 coronary artery bypass grafting patients. Poor glycemic control was defined as greater than 33% of continuous glucose monitoring values < 70 and > 180 mg/dL (group 1);and then these patients were reevaluated with a less strict definition of poor glycemic control with greater than 25% of continuous glucose values < 70 and > 180 mg/dL (group 2). In group 1 4/10 (40.0%) whose glucose was not well controlled went to an extended care post discharge facility as opposed to 6/49 (12.2%) that were well controlled. In reevaluation as group 2, 5/14 (35.7%) whose glucose was not well controlled went to an extended care post discharge facility as opposed to 5/45 (11.1%) who were well controlled. Admission to a post discharge facility was increased in patients with poor glycemic control p = 0.045 and p = 0.042 for group 1 and group 2, and with odds ratios of 4.8 (95% CI 1.0-22.5) and 4.4 (95% CI 1.0-19.4), respectively. © 2018 The Author(s).

关键词： Algorithms Clinical decision support systems Extended care facilities Glucose Monitoring Thoracic surgery

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Crimp analysis of connective tissue using quantitative second-harmonic generation microscopy

Crimp analysis of connective tissue using quantitative secon...

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Frontiers in Optics, FiO 2017

作者： Lee, Woowon Rahman, Hafizur Kersh, Mariana E. Toussaint, Kimani C. University of Illinois at Urbana-Champaign Department of Mechanical Science and Engineering UrbanaIL United States University of Illinois at Urbana-Champaign PROBE Lab UrbanaIL United States University of Illinois at Urbana-Champaign Affiliate in the Departments of Electrical and Computer Engineering and Bioengineering UrbanaIL United States

We assess the capacity for second-harmonic generation (SHG) microscopy by quantitatively characterizing collagen fiber crimp pattern in the posterior cruciate ligament (PCL) and disclosing the 3D structure in PCL and ... 详细信息

ISBN: (纸本)9781943580330

关键词： Harmonic generation

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The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI

arXiv

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arXiv 2024年

作者： Correia de Verdier, Maria Saluja, Rachit Gagnon, Louis LaBella, Dominic Baid, Ujjwall Tahon, Nourel Hoda Foltyn-Dumitru, Martha Zhang, Jikai Alafif, Maram Baig, Saif Chang, Ken D’Anna, Gennaro Deptula, Lisa Gupta, Diviya Haider, Muhammad Ammar Hussain, Ali Iv, Michael Kontzialis, Marinos Manning, Paul Moodi, Farzan Nunes, Teresa Simon, Aaron Sollmann, Nico Vu, David Adewole, Maruf Albrecht, Jake Anazodo, Udunna Chai, Rongrong Chung, Verena Faghani, Shahriar Farahani, Keyvan Kazerooni, Anahita Fathi Iglesias, Eugenio Kofler, Florian Li, Hongwei Linguraru, Marius George Menze, Bjoern Moawad, Ahmed W. Velichko, Yury Wiestler, Benedikt Altes, Talissa Basavasagar, Patil Bendszus, Martin Brugnara, Gianluca Cho, Jaeyoung Dhemesh, Yaseen Fields, Brandon K.K. Garrett, Filip Gass, Jaime Hadjiiski, Lubomir Hattangadi-Gluth, Jona Hess, Christopher Houk, Jessica L. Isufi, Edvin Layfield, Lester J. Mastorakos, George Mongan, John Nedelec, Pierre Nguyen, Uyen Oliva, Sebastian Pease, Matthew W. Rastogi, Aditya Sinclair, Jason Smith, Robert X. Sugrue, Leo P. Thacker, Jonathan Vidic, Igor Villanueva-Meyer, Javier White, Nathan S. Aboian, Mariam Conte, Gian Marco Dale, Anders Sabuncu, Mert R. Seibert, Tyler M. Weinberg, Brent Abayazeed, Aly Huang, Raymond Turk, Sevcan Rauschecker, Andreas M. Farid, Nikdokht Vollmuth, Philipp Nada, Ayman Bakas, Spyridon Calabrese, Evan Rudie, Jeffrey D. Department of Surgical Sciences Section of Neuroradiology Uppsala University Sweden Department of Radiology University of California San DiegoCA United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine United States Department of Pathology and Laboratory Medicine Indiana Univeristy IN United States Department of Neuroradiology Heidelberg University Germany University of Laval Quebec CityQC Canada Department of Radiation Oncology Duke University DurhamNC United States Radiology Department University of Missouri ColumbiaMO United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Radiology Nassau Medical Center East MeadowNY United States Department of Radiology Stanford University CA United States Neuroimaging Unit ASST Ovest Milanese Legnano Milan Italy Ross University School of Medicine Bridgetown Barbados Department of Radiology University of California Los AngelesCA United States C.M.H. Lahore Medical College Institute of Dentistry Lahore Pakistan Department of Imaging Sciences University of Rochester Medical Center RochesterNY United States Department of Radiology Northwestern University ChicagoIL United States Department of Radiology VA San Diego Medical Center United States School of Medicine Iran University of Medical Sciences Tehran Iran Neuroradiology Department Hospital Garcia de Orta Unidade Local de Saúde de Almada Seixal EPE Almada Portugal Imaging Department Hospital da Luz Lisboa Lisbon Portugal Department of Radiation Oncology University of California IrvineCA United States Department of Diagnostic and Interventional Radiology University Hospital Ulm Ulm Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum Rechts der Isar Technical University of Munich Munich Germany Department of Radiology Scripps Clin

Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care. © 2024, CC BY.

关键词： Magnetic resonance imaging

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