Article
Meteorology & Atmospheric Sciences
Troy Arcomano, Istvan Szunyogh, Alexander Wikner, Jaideep Pathak, Brian R. Hunt, Edward Ott
Summary: This paper describes the implementation of a combined hybrid-parallel prediction approach on a low-resolution atmospheric global circulation model. The hybrid model, which combines a physics-based numerical model with a machine learning component, produces more accurate forecasts for various atmospheric variables compared to the host model. Furthermore, the hybrid model exhibits smaller systematic errors and more realistic temporal variability in simulating the climate.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Cardiac & Cardiovascular Systems
Dario De Marinis, Dominik Obrist
Summary: The proposed data assimilation methodology aims to enhance the spatial and temporal resolution of voxel-based data obtained from biomedical imaging modalities, specifically focusing on turbulent blood flow assessment in large vessels. The methodology, utilizing a Stochastic Ensemble Kalman Filter approach, combines observed flow fields with numerical simulations to improve the accuracy of flow field predictions. Validation against canonical flows and application to a clinically relevant scenario demonstrate the potential of the method to enhance 4D flow MRI data for future use.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2021)
Article
Multidisciplinary Sciences
Kevin Raeder, Timothy J. Hoar, Mohamad El Gharamti, Benjamin K. Johnson, Nancy Collins, Jeffrey L. Anderson, Jeff Steward, Mick Coady
Summary: An ensemble Kalman filter reanalysis data set with a global, 80 member ensemble spanning from 2011 to 2019 is archived, providing opportunities for robust statistical analysis and machine learning training.
SCIENTIFIC REPORTS
(2021)
Article
Meteorology & Atmospheric Sciences
Jeffrey S. Whitaker, Anna Shlyaeva, Stephen G. Penny
Summary: This study compares two methods for incorporating a time-invariant, high-rank covariance estimate in an ensemble-based data assimilation system: the hybrid-covariance approach and the hybrid-gain approach. The results show that the simpler and less expensive hybrid-gain approach can achieve similar performance if the incremental normal-mode balance constraint applied to the ensemble-part of the hybrid-covariance update is turned off.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Meteorology & Atmospheric Sciences
Haohao Sun, Lili Lei, Zhengyu Liu, Liang Ning, Zhe-Min Tan
Summary: An analog offline ensemble Kalman filter (AOEnKF) is proposed, which constructs ensemble priors from a control climate simulation for each assimilation time based on an analog criterion using proxy observations. AOEnKF generates smaller posterior errors and requires much less computational cost compared to the online cycling EnKF (CEnKF). It has the advantages of having a more accurate prior ensemble mean and flow-dependent background error covariances compared to the commonly applied offline EnKF (OEnKF).
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Meteorology & Atmospheric Sciences
Lars Nerger
Summary: The study introduces a hybrid filter combining LETKF and NETF with the performance improved by adjusting the hybrid weight. Results show that a hybrid variant applying NETF followed by LETKF yields the best results in complex nonlinear models. Calculating the hybrid weight based on skewness, kurtosis, and effective sample size reduces estimation errors and enhances stability of the hybrid filter.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2022)
Article
Water Resources
Andrew Pensoneault, Witold F. Krajewski, Nicolas Velasquez, Xueyu Zhu, Ricardo Mantilla
Summary: This paper discusses the application of data assimilation techniques in hydrology, focusing on the potential of EnKF and its extensions in sequential state estimation and Bayesian inverse problems. The authors improve the streamflow in a virtual catchment using the EKI algorithm and demonstrate its favorable performance.
ADVANCES IN WATER RESOURCES
(2023)
Article
Water Resources
Johannes Keller, Harrie-Jan Hendricks Franssen, Wolfgang Nowak
Summary: Parameter estimation is crucial in geosciences, and the evaluation of the pilot point ensemble Kalman filter (PP-EnKF) shows that it performs well for parameter estimation in different settings, ranking higher than traditional EnKF methods.
ADVANCES IN WATER RESOURCES
(2021)
Article
Green & Sustainable Science & Technology
Xin He, Changjiang Yuan, Haoran Gao, Yaqing Chen, Rui Zhao
Summary: This study uses the ensemble Kalman filter algorithm to recalibrate the SA turbulence model constants by integrating NASA's experimental particle image velocimetry (PIV) data with a sample library generated using Latin hypercube sampling to accurately predict the flow characteristics of subsonic jet exhaust. The recalibrated model constants effectively improve the prediction of jet flow characteristics, and have important implications for acquiring high-fidelity data on rear engine jet flows after takeoff, enabling precise determination of safety separation distances, and enhancing the operational efficiency of airports.
Article
Engineering, Civil
Lingzhong Kong, Ruibin Chen, Hongwu Tang, Saiyu Yuan, Qian Yang, Qingfeng Ji
Summary: Accurate water level forecasts are crucial for river management. The proposed joint ensemble Kalman filter (EnKF) framework allows concurrent estimation of flow disturbances and water levels, improving prediction accuracy. Even when the location of the disturbance point is unknown, the introduction of a fictitious lateral outflow enables estimation of the flow disturbance process. Synthetic case study results demonstrate superior water level estimations compared to scenarios without considering flow disturbances.
JOURNAL OF HYDROLOGY
(2023)
Article
Engineering, Civil
Behmard Sabzipour, Richard Arsenault, Magali Troin, Jean-Luc Martel
Summary: Data assimilation is an important step in improving hydrological model predictions. This study aims to identify optimal seasonal parameterizations to reduce uncertainty in initial conditions in a snow-dominated catchment in Canada. Sensitivity analysis shows that forecast performance is sensitive to individual hyperparameters and the choice of state variables.
JOURNAL OF HYDROLOGY
(2023)
Article
Environmental Sciences
G. Piazzi, G. Thirel, C. Perrin, O. Delaigue
Summary: Skillful streamflow forecasts are crucial for water-related applications, with a growing emphasis on improving initial condition estimates through data assimilation. This study assesses the sensitivity of DA-based IC estimation to various uncertainties and model updates over 232 watersheds in France. The comparison of two ensemble-based techniques shows that accurate routing store estimates benefit the DA-based IC estimation, with the EnKF outperforming the PF in forecasting meteorological uncertainty.
WATER RESOURCES RESEARCH
(2021)
Article
Engineering, Ocean
Shintaro Gomi, Tsutomu Takagi, Katsuya Suzuki, Rika Shiraki, Ichiya Ogino, Shigeru Asaumi
Summary: A control method for changing the geometry of a fishing net was proposed, utilizing data assimilation to estimate unknown parameters and achieve the intended net geometry. The automatic control system was validated through numerical simulation experiments, demonstrating the successful control of net geometry using the extended Kalman filter.
APPLIED OCEAN RESEARCH
(2021)
Article
Mechanics
Zhiwen Deng, Chuangxin He, Yingzheng Liu
Summary: This paper focuses on the optimal sensor placement strategy based on a deep neural network for turbulent flow recovery within the data assimilation framework of the ensemble Kalman filter. The results demonstrate the effectiveness and robustness of the proposed strategy, showing that RANS models with EnKF augmentation were substantially improved over their original counterparts. The study concludes that the DNN-based OSP with the selection of the five most sensitive sensors can efficiently reduce the number of sensors while achieving similar or better assimilated performance.
Article
Engineering, Civil
Teng Xu, J. Jaime Gomez-Hernandez, Zi Chen, Chunhui Lu
Summary: Understanding a contaminant source is crucial for managing a polluted aquifer, but source information may be unavailable when pollutants are detected. The Ensemble Smoother with Multiple Data Assimilation (ES-MDA) is proposed as a more efficient solution than the restart Ensemble Kalman Filter (r-EnKF), but requires a large number of assimilations to achieve the same level of accuracy.
JOURNAL OF HYDROLOGY
(2021)
Article
Biology
Soumick Chatterjee, Mario Breitkopf, Chompunuch Sarasaen, Hadya Yassin, Georg Rose, Andreas Nuernberger, Oliver Speck
Summary: This study proposes a deep learning-based framework for MR image reconstruction, which can effectively reconstruct highly undersampled images without artifact. The framework shows robustness to different sampling patterns and preserves brain pathology during training.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Neurosciences
Meng Li, Lena Vera Danyeli, Lejla Colic, Gerd Wagner, Stefan Smesny, Tara Chand, Xin Di, Bharat B. Biswal, Jorn Kaufmann, Juergen R. Reichenbach, Oliver Speck, Martin Walter, Zuemruet Duygu Sen
Summary: Reproducible resting-state functional connectivity patterns and their alterations have significant implications in neuropsychiatric research. This study utilizes multimodal imaging and magnetic resonance spectroscopy to investigate the correlation between regional neurotransmitter levels and rsFC strength, providing insights into the modulation of interaction between brain regions at a macroscopic level.
HUMAN BRAIN MAPPING
(2022)
Article
Biology
Saskia Bollmann, Hendrik Mattern, Michael Bernier, Simon D. Robinson, Daniel Park, Oliver Speck, Jonathan R. Polimeni, Saad Jbabdi
Summary: This article presents a new method using TOF-MRA imaging technique to observe the arteries on the surface of the human brain. Through experimental verification, it is demonstrated that this method can detect small pial arteries with extremely high resolution. The research results indicate that the observation of pial arteries is no longer limited by blood flow velocity, but by image resolution.
Article
Clinical Neurology
Marios K. Georgakis, Rong Fang, Marco Duering, Frank A. Wollenweber, Felix J. Bode, Sebastian Stoesser, Christine Kindlein, Peter Hermann, Thomas G. Liman, Christian H. Nolte, Lucia Kerti, Benno Ikenberg, Kathleen Bernkopf, Holger Poppert, Wenzel Glanz, Valentina Perosa, Daniel Janowitz, Michael Wagner, Katja Neumann, Oliver Speck, Laura Dobisch, Emrah Duezel, Benno Gesierich, Anna Dewenter, Annika Spottke, Karin Waegemann, Michael Goertler, Silke Wunderlich, Matthias Endres, Inga Zerr, Gabor Petzold, Martin Dichgans
Summary: The global burden of small vessel disease (SVD) predicts cognitive and functional outcomes in stroke patients, but the current score used for assessment does not improve prediction capability. Assessing the severity of SVD lesions adds value in predicting outcomes beyond known predictors.
ALZHEIMERS & DEMENTIA
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yi-Hang Tung, Myung-Ho In, Sinyeob Ahn, Oliver Speck
Summary: VAT-PSF-EPI is a novel spin-echo EPI-based sequence that enables fast high-resolution diffusion imaging at ultrahigh field. It effectively suppresses distortion and corrects the introduced image blurring through PSF encoding. Up to fourfold acceleration can be achieved compared to standard PSF-EPI.
MAGNETIC RESONANCE IN MEDICINE
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Natalie Schoen, Frank Seifert, Johannes Petzold, Gregory J. Metzger, Oliver Speck, Bernd Ittermann, Sebastian Schmitter
Summary: This study presents electromagnetic simulation setups to analyze the effects of respiration on B-1(+) and E-fields, local SAR, and safety limits for 7T cardiac imaging. The results show that respiration affects the distribution of B-1(+) and the peak spatial SAR, emphasizing the need to consider respiratory motion in the safety analysis of RF coils applied to the human thorax.
MAGNETIC RESONANCE IN MEDICINE
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Layla Tabea Riemann, Christoph Stefan Aigner, Ralf Mekle, Oliver Speck, Georg Rose, Bernd Ittermann, Sebastian Schmitter, Ariane Fillmer
Summary: The study demonstrated the use of the 2SPECIAL sequence coupled with the novel voxel-GRAPPA decomposition technique to simultaneously acquire spectroscopic signals from two MRS voxels without significant loss in SNR, reducing total scan time by 21.6%. The voxel-GRAPPA algorithm properly reassigned signal components to their respective regions, showing comparable performance to the SENSE-based algorithm in terms of leakage and Cramer-Rao lower bounds for in vivo applications.
MAGNETIC RESONANCE IN MEDICINE
(2022)
Article
Biology
Soumick Chatterjee, Alessandro Sciarra, Max Duennwald, Pavan Tummala, Shubham Kumar Agrawal, Aishwarya Jauhari, Aman Kalra, Steffen Oeltze-Jafra, Oliver Speck, Andreas Nuernberger
Summary: This research proposes a more robust unsupervised anomaly detection system that can detect anomalies such as tumors in brain MRIs. The system utilizes a compact version of the context-encoding VAE model, combined with pre and post-processing steps, creating a pipeline that outperforms other methods in clinical data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Letter
Clinical Neurology
Stefanie Schreiber, Anna-Charlotte John, Cornelius J. Werner, Stefan Vielhaber, Hans-Jochen Heinze, Oliver Speck, Jens Wuerfel, Daniel Behme, Hendrik Mattern
JOURNAL OF NEUROLOGY
(2023)
Article
Biophysics
Johannes Petzold, Sebastian Schmitter, Berk Silemek, Lukas Winter, Oliver Speck, Bernd Ittermann, Frank Seifert
Summary: This study integrates the safety assessment of implant carriers in MRI with pTx technology. It proposes a comprehensive safety concept that combines real-time monitoring with physical sensors for quantifying implant-related heating. Simulation experiments and optimization algorithms are used to find the best excitation scheme that ensures both overall and local safety.
NMR IN BIOMEDICINE
(2023)
Article
Biology
Hana Haseljic, Soumick Chatterjeeb, Robert Frysch, Vojtech Kulvait, Vladimir Semshchikov, Bennet Hensenj, Frank Wackerj, Inga Brueschk, Thomas Wernckej, Oliver Speck, Andreas Nuernberger, Georg Rose
Summary: Model-based reconstruction with TST improves dynamic perfusion imaging of the liver using CBCT. Accurate liver segmentation is required for applying TST with prior knowledge from CT perfusion data. Turbolift learning sequentially trains a modified Attention UNet on different liver segmentation tasks, achieving significant improvement in liver segmentation from CBCT TST. This research demonstrates the potential of segmenting the liver from CT, CBCT, and CBCT TST for visualizing and evaluating perfusion maps in liver disease treatment.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Philipp Ernst, Soumick Chatterjee, Georg Rose, Oliver Speck, Andreas Nuernberger
Summary: Computed tomography (CT) and magnetic resonance imaging (MRI) are widely used clinical imaging modalities for non-invasive diagnosis, but both have certain problems. This paper proposes a unified solution for sparse CT and undersampled radial MRI reconstruction and improves the accuracy and reconstruction speed of the Primal-Dual network.
Article
Neurosciences
Luisa Herrmann, Johanna Ade, Anne Kuehnel, Annina Widmann, Liliana Ramona Demenescu, Meng Li, Nils Opel, Oliver Speck, Martin Walter, Lejla Colic
Summary: High childhood emotional maltreatment (CM-EMO) is associated with an increased risk for psychopathology, potentially through alterations in gamma-Aminobutyric acid (GABA). The pregenual anterior cingulate cortex (pgACC) is an important brain region for emotion processing, and its' GABA levels are implicated in mood and anxiety disorders. This study examined the association between self-reported CM-EMO in adulthood and GABA+ levels in the pgACC, finding a negative relationship between CM-EMO-NEG and GABA+/tCr in the pgACC.
NEUROBIOLOGY OF STRESS
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Johannes Petzold, Sebastian Schmitter, Berk Silemek, Lukas Winter, Oliver Speck, Bernd Ittermann, Frank Seifert
Summary: This study investigates the safety and performance aspects of parallel-transmit RF control modes for a body coil at B-0 <= 3T using electromagnetic simulations. The results show that PTx body coils can be used safely at B-0 <= 3T, but uncertainties in patient anatomy must be taken into account.
MAGNETIC RESONANCE IN MEDICINE
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Mariia Anikeeva, Maitreyi Sangal, Oliver Speck, Graham Norquay, Maaz Zuhayra, Ulf Luetzen, Josh Peters, Olav Jansen, Jan-Bernd Hoevener
Summary: This review discusses the use of hyperpolarized xenon-129 (Xe-MRI) for pulmonary MRI and its unique insights into lung microstructure and function.
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)