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An effective method for quantification,visualization,and analysis of 3D cell shape during early embryogenesis

Quantitative Biology 2025年第1期13卷 113-126页

作者： Zelin Li Zhaoke Huang Jianfeng Cao Guoye Guan Zhongying Zhao Hong Yan Department of Electrical Engineering City University of Hong KongHong KongChina Centre for Intelligent Multidimensional Data Analysis Limited Hong KongChina Center for Quantitative Biology Peking UniversityBeijingBeijingChina Department of Systems Biology Harvard Medical SchoolBostonMassachusettsUSA Department of Data Science Dana-Farber Cancer InstituteBostonMassachusettsUSA Department of Biology Hong Kong Baptist UniversityHong KongChina State Key Laboratory of Environmental and Biological Analysis Hong Kong Baptist UniversityHong KongChina

Embryogenesis is the most basic process in developmental *** and simply quantifying cell shape is challenging for the complex and dynamic 3D embryonic *** descriptors such as volume,surface area,and mean curvature often fall short,providing only a global view and lacking in local detail and reconstruction *** this,we introduce an effective integrated method,3D Cell Shape Quantification(3DCSQ),for transforming digitized 3D cell shapes into analytical feature vectors,named eigengrid(proposed grid descriptor like eigen value),eigenharmonic,and *** uniquely combine spherical grids,spherical harmonics,and principal component analysis for cell shape *** demonstrate 3DCSQ’s effectiveness in recognizing cellular morphological phenotypes and clustering *** to Caenorhabditis elegans embryos of 29 living embryos from 4-to 350-cell stages,3DCSQ identifies and quantifies biologically reproducible cellular patterns including distinct skin cell *** also provide automatically cell shape lineaging analysis *** method not only systematizes cell shape description and evaluation but also monitors cell differentiation through shape changes,presenting an advancement in biological imaging and analysis.

关键词： Caenorhabditis elegans(C.elegans) cell shape quantification eigen features(eigengrid eigenharmonic&eigenspectrum) lineage analysis morphological reproducibility spherical harmonics(SPHARM)

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Assessing the Performance of Point Sensor Continuous Monitoring systems at Midstream Natural Gas Compressor Stations

ACS ES&T Air

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ACS ES&T Air 2025年第4期2卷 466-475页

作者： Shuting Lydia Yang Arvind P. Ravikumar Center for Energy and Environmental Systems Analysis (CEESA) The University of Texas at Austin Austin Texas 78712 United States；Energy Emissions Modeling and Data Lab The University of Texas at Austin Austin Texas 78712 United States Center for Energy and Environmental Systems Analysis (CEESA) The University of Texas at Austin Austin Texas 78712 United States；Energy Emissions Modeling and Data Lab The University of Texas at Austin Austin Texas 78712 United States；Department of Petroleum and Geosystems Engineering The University of Texas at Austin Austin Texas 78712 United States

Continuous Monitoring systems (CMS) are a promising technology to detect and quantify intermittent and high-volume methane emissions across the oil and gas supply chain. This is particularly salient at midstream compressor stations, where the contribution of short-duration emission events to total emissions makes survey-type technologies less suitable for developing accurate measurement-informed inventories. In this work, we report on the first concurrent and long-term test of five CMS technologies to detect, localize, and quantify methane emission from two major types of midstream compressor stations found in the US – a turbine-only station and an engine-only station. We find that CMS technologies can distinguish between different operational states of engine-driven compressors with large CH4emissions contribution from compressor exhaust. Combining known events at these facilities with in situ controlled releases, we observe that all CMS technologies generally struggle in identifying short-duration (<30 min) or low emission rate (relative to baseline) events. Critically, we find that positive event detection, based on analysis of underlying methane signals, frequently did not translate to alerts sent to the operators. Deployment of CMS at midstream compressor stations must proceed with caution based on specific applications, site configuration, and the nature of baseline *** study reports on the concurrent test of five Continuous Monitoring systems to detect, localize, and quantify methane emission and informs the practical deployment and limits of Continuous Monitoring systems at midstream natural gas facilities.

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On structural and practical identifiability: Current status and update of results

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Current Opinion in systems Biology 2025年 41卷

作者： Heinrich, Mio Rosenblatt, Marcus Wieland, Franz-Georg Stigter, Hans Timmer, Jens University Freiburg Institute of Physics Germany CIBSS Centre for Integrative Biological Signalling Studies Freiburg Germany Freiburg Center for Data Analysis and Modeling Freiburg Germany Wageningen University Wageningen Netherlands

Identifiability of parameters in dynamical systems is a fundamental concept of mathematical modelling in systems biology and systems medicine. Both the structurally inherent identifiability of parameters in models and the practical identifiability of parameters, which arises from insufficient available data, play crucial roles in the development of useful models. Here, we provide an overview of recent developments in the field of structural identifiability analysis of models based on ordinary differential equations, emphasising its importance for accurate parameter estimation. We extend an existing benchmark study by comparing the methods for structural identifiability analysis with the recently developed StrucID, showing it to be a fast, efficient and intuitive algorithm. Furthermore, this review highlights the challenges in practical identifiability analysis and the need for benchmarking with real-world models using experimental data. The potential benefits of standardising documentation for benchmarking models with experimental data and practical non-identifiabilities are stressed. © 2025 The Author(s)

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Developing Measurement-Informed Methane Emissions Inventory Estimates at Midstream Compressor Stations

ACS ES&T Air

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ACS ES&T Air 2025年第3期2卷 358-367页

作者： Arvind P. Ravikumar Hugh Li Shuting Lydia Yang Mackenzie L. Smith Center for Energy and Environmental Systems Analysis (CEESA) The University of Texas at Austin Austin Texas 78712 United States；Energy Emissions Modeling and Data Lab The University of Texas at Austin Austin Texas 78712 United States；Department of Petroleum and Geosystems Engineering The University of Texas at Austin Austin Texas 78712 United States Department of Systems Engineering Harrisburg University of Science and Technology Harrisburg Pennsylvania 17101 United States Center for Energy and Environmental Systems Analysis (CEESA) The University of Texas at Austin Austin Texas 78712 United States；Energy Emissions Modeling and Data Lab The University of Texas at Austin Austin Texas 78712 United States Champion X The Woodlands Texas 77381 United States

Natural gas transmissions and storage compressor stations account for the largest share of anthropogenic methane (CH4) emissions in New York State (NYS). Yet, NYS’s CH4emissions inventory is based on measurements that are a decade old and potentially unlikely to be representative of NYS operations. Here, we present results from a comprehensive, multiscale aerial CH4measurement campaign across all NYS transmission and storage compressor stations. We find a skewed emissions distribution, with 20% of stations accounting for 74% of total CH4emissions. Emissions at engine-driven compressor stations are, on average, 3–4× higher than emissions at turbine-driven compressor stations, thus demonstrating the need for separate emissions factors for engine- and turbine-drive compressor stations. Overall, measurement-informed emissions inventory from midstream transmission and storage compressor stations in NYS is 72% and 69% lower than the current NYS inventory, respectively. We estimate updated emissions factors of 464 [95% CI: 162–920] metric ton (MT) CH4/station/yr and 139 [97, 191] MT CH4/station/yr for engine- and turbine-based transmission compressor stations, respectively. Similarly, we estimate an updated emissions factor of 413 [164, 733] MT CH4/station/yr for engine-based storage compressor stations. These updated emissions factors, along with improved activity data, enable effective reconciliation of NYS inventory with measured *** study reports on the first large-scale measurement of methane emissions from midstream compressor stations in New York and provides a scalable approach to reconcile and update official emission inventories.

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Good Modelling Software Practices

arXiv

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arXiv 2024年

作者： Lemmen, Carsten Sommer, Philipp Sebastian Institute of Coastal Systems - Analysis and Modeling Helmholtz-Zentrum Hereon Max-Planck-Str. 1 Geesthacht21502 Germany Institute of Carbon Cycles Helmholtz Coastal Data Center Helmholtz-Zentrum Hereon Max-Planck-Str. 1 Geesthacht21502 Germany

Frequently in socio-environmental sciences, models are used as tools to represent, understand, project and predict the behaviour of these complex systems. Along the modelling chain, Good Modelling Practices have been evolving that ensure—amongst others—that models are transparent and their results replicable. Whenever such models are represented in software, Good Modelling meet Good Software Practices, such as a tractable development workflow, good code, collaborative development and governance, continuous integration and deployment;and they meet Good Scientific Practices, such as attribution of copyrights and acknowledgement of intellectual property, publication of a software paper and archiving. Too often in existing socio-environmental model software, these practices have been regarded as an add-on to be considered at a later stage only;modellers have shied away from publishing their model as open source out of fear that having to add good practices is too demanding. We here argue for making a habit of following a list of simple and not so simple practices early on in the implementation of the model life cycle. We contextualise cherry-picked and hands-on practices for supporting Good Modelling Practice, and we demonstrate their application in the example context of the Viable North Sea fisheries socio-ecological systems model. © 2024, CC BY.

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An effective method for quantification, visualization, and analysis of 3D cell shape during early embryogenesis

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Quantitative Biology 2025年第1期 134-147页

作者： Zelin Li Zhaoke Huang Jianfeng Cao Guoye Guan Zhongying Zhao Hong Yan Department of Electrical Engineering City University of Hong Kong Centre for Intelligent Multidimensional Data Analysis Limited Center for Quantitative Biology Peking University Department of Systems Biology Harvard Medical School Department of Data Science Dana-Farber Cancer Institute Department of Biology Hong Kong Baptist University State Key Laboratory of Environmental and Biological Analysis Hong Kong Baptist University

Embryogenesis is the most basic process in developmental *** and simply quantifying cell shape is challenging for the complex and dynamic 3D embryonic cells. Traditional descriptors such as volume,surface area, and mean curvature often fall short, providing only a global view and lacking in local detail and reconstruction capability. Addressing this, we introduce an effective integrated method, 3D Cell Shape Quantification(3DCSQ), for transforming digitized 3D cell shapes into analytical feature vectors, named eigengrid(proposed grid descriptor like eigen value), eigenharmonic, and eigenspectrum. We uniquely combine spherical grids, spherical harmonics, and principal component analysis for cell shape quantification. We demonstrate 3DCSQ's effectiveness in recognizing cellular morphological phenotypes and clustering cells. Applied to Caenorhabditis elegans embryos of 29 living embryos from 4-to 350-cell stages,3DCSQ identifies and quantifies biologically reproducible cellular patterns including distinct skin cell deformations. We also provide automatically cell shape lineaging analysis program. This method not only systematizes cell shape description and evaluation but also monitors cell differentiation through shape changes, presenting an advancement in biological imaging and analysis.

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Identification of soybean in Argentina using Sentinel-2 composite images

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International Journal of Agricultural and biological Engineering 2024年第5期17卷 266-274页

作者： Linsheng Huang Yue Chen Yuhao Pan Zihang Lou Shijun Zheng Xiaoyang Zhang Le Yu Shengwei Liu Dailiang Peng National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China Research Area of Ecology and Biodiversity School of Biological SciencesThe University of Hong KongPokfulamHong KongChina College of Environmental and Resource Sciences Zhejiang UniversityHangzhou 310058China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China Geospatial Sciences Center of Excellence Department of Geography&Geospatial SciencesSouth Dakota State UniversityBrookingsSD 57007USA Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing 100084China Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing 100084China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo 454000HenanChina

Soybean is one of the most important oil crops, and Argentina is the third-largest soybean producer in the world, accounting for 17% of the global soybean yield. Timely and accurate information on soybean spatial distribution is critical for ensuring global food security. Sentinel-2 multispectral data and machine learning classification models are used to investigate the potential of soybean identification in the early stage of the growing season in Argentina, with the help of Google Earth Engine (GEE). The earliest time window and optimal feature set for soybean identification are explored. Results are as follows: 1) the random forest (RF) classification model demonstrated the highest level of classification accuracy compared to the backpropagation neural network (BPNN), support vector machine (SVM), and naive Bayes (NB) models;2) Soybean can be accurately identified as early as the end of February (filling stage), which is approximately one month before harvest;3) The optimal feature-subset can reduce the amount of input data by 80% while maintaining high classification accuracy. The overall accuracy (OA) of the RF classification model is 85.87%, and the relative error between the estimated soybean planting area and the agricultural statistics is 3.45%. This study provided a high-precision method for early-season identification of soybeans over large scales. The results can provide a data support for early futures trading and agricultural insurance, as well as a reference for policy-making to ensure global soybean food security.

关键词： soybean machine learning time window feature selection Sentinel-2 Google Earth Engine

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modeling Arctic Boundary Layer Cloud Streets at Grey-zone Resolutions

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Advances in Atmospheric Sciences 2020年第1期37卷 42-56页

作者： Hui-Wen LAI Fuqing ZHANG Eugene E.CLOTHIAUX David R.STAUFFER Brian J.GAUDET Johannes VERLINDE Deliang CHEN Department of Meteorology and Atmospheric Science and Center for Advanced Data Assimilation and Predictability TechniquesThe Pennsylvania State UniversityUniversity ParkPennsylvania 16802USA Department of Earth Sciences University of GothenburgGothenburg 40530Sweden Earth Systems Analysis and Modeling Pacific Northwest National LaboratoryRichlandWashington 99354USA

To better understand how model resolution affects the formation of Arctic boundary layer clouds,we investigated the influence of grid spacing on simulating cloud streets that occurred near Utqiaġvik(formerly Barrow),Alaska,on 2 May 2013 and were observed by MODIS(the Moderate Resolution Imaging Spectroradiometer).The Weather Research and Forecasting model was used to simulate the clouds using nested domains with increasingly fine resolution ranging from a horizontal grid spacing of 27 km in the boundary-layer-parameterized mesoscale domain to a grid spacing of 0.111 km in the large-eddy-permitting *** investigated the model-simulated mesoscale environment,horizontal and vertical cloud structures,boundary layer stability,and cloud properties,all of which were subsequently used to interpret the observed roll-cloud *** model resolution led to a transition from a more buoyant boundary layer to a more shear-driven turbulent boundary *** clouds were stratiform-like in the mesoscale domain,but as the model resolution increased,roll-like structures,aligned along the wind field,appeared with ever smaller wavelengths.A stronger vertical water vapor gradient occurred above the cloud layers with decreasing grid *** fixed model grid spacing at 0.333 km,changing the model configuration from a boundary layer parameterization to a large-eddy-permitting scheme produced a more shear-driven and less unstable environment,a stronger vertical water vapor gradient below the cloud layers,and the wavelengths of the rolls decreased *** this study,only the large-eddy-permitting simulation with gird spacing of 0.111 km was sufficient to model the observed roll clouds.

关键词： cloud streets large eddy simulation grey zone Arctic clouds model resolution

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Adaptive aggregation of Monte Carlo augmented decomposed filters for efficient group-equivariant convolutional neural network

arXiv

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arXiv 2023年

作者： Zhao, Wenzhao Wichtmann, Barbara D. Albert, Steffen Maurer, Angelika Zöllner, Frank G. Attenberger, Ulrike Hesser, Jürgen Interdisciplinary Center for Scientific Computing Mannheim Institute for Intelligent Systems in Medicine Medical Faculty Mannheim Heidelberg University Germany Department of Diagnostic and Interventional Radiology University Hospital Bonn Germany Computer Assisted Clinical Medicine Mannheim Institute for Intelligent Systems in Medicine Medical Faculty Mannheim Heidelberg University Germany Interdisciplinary Center for Scientific Computing Central Institute for Computer Engineering CSZ Heidelberg Center for Model-Based AI Data Analysis and Modeling in Medicine Mannheim Institute for Intelligent Systems in Medicine Medical Faculty Mannheim Heidelberg University Germany

Group-equivariant convolutional neural networks (G-CNN) heavily rely on parameter sharing to increase CNN’s data efficiency and performance. However, the parameter-sharing strategy greatly increases the computational burden for each added parameter, which hampers its application to deep neural network models. In this paper, we address these problems by proposing a non-parameter-sharing approach for group equivariant neural networks. The proposed methods adaptively aggregate a diverse range of filters by a weighted sum of stochastically augmented decomposed filters. We give theoretical proof about how the continuous group convolution can be approximated by our methods. Our method applies to both continuous and discrete groups, where the augmentation is implemented using Monte Carlo sampling and bootstrap resampling, respectively. We demonstrate that our methods serve as an efficient extension of standard CNN. Experiments on group equivariance tests show how our methods can achieve superior performance to parameter-sharing group equivariant networks. Experiments on image classification and image denoising tasks show that in certain scenarios, with a suitable set of filter bases, our method helps improve the performance of standard CNNs and build efficient lightweight image denoising networks. The code will be available at https://***/ZhaoWenzhao/MCG_CNN. Copyright © 2023, The Authors. All rights reserved.

关键词： Image denoising

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Detecting frequency modulation in stochastic time-series data

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Physical Review E 2022年第2期106卷 024204-024204页

作者： Adrian L. Hauber Christian Sigloch Jens Timmer Institute of Physics University of Freiburg 79104 Freiburg Germany Freiburg Center for Data Analysis and Modeling (FDM) University of Freiburg 79104 Freiburg Germany Developmental Biology Institute Biology I Faculty of Biology University of Freiburg 79104 Freiburg Germany Spemann Graduate School of Biology and Medicine (SGBM) University of Freiburg 79104 Freiburg Germany Centre for Integrative Biological Signalling Studies (CIBSS) University of Freiburg 79104 Freiburg Germany

We propose a statistical test to identify nonstationary frequency-modulated stochastic processes from time-series data. Our method uses the instantaneous phase as a discriminatory statistics with reliable critical values derived from surrogate data. We simulated an oscillatory second-order autoregressive process to evaluate the size and power of the test. We found that the test we propose is able to correctly identify more than 99% of nonstationary data when the frequency of the simulated data is doubled after the first half of the time series. Our method is easily interpretable, computationally cheap, and does not require choosing hyperparameters that are dependent on the data.

关键词： Statistical methods Stochastic processes & statistics

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