Rui Ma

In nature, active matter, such as worms or dogs, tend to spontaneously form a stable rotational cluster when they flock to the same food source on an unregulated and unconfined surface. {In this paper we present an $n$-node flexible active matter model to study the collective motion due to the flocking of individual agents on a two-dimensional surface, and confirm that there exists a spontaneous stable cluster rotation synchronizing with a chirality produced by the alignment of their bodies under the impetus of the active force.} A prefactor of 1.86 is obtained for the linear relationship between normalized angular velocity and chirality. The angular velocity of such a rotation is found to be dependent on the individual flexibility, the number of nodes in each individual, and the magnitude of the active force. The conclusions well explain the spontaneous stable rotation of clusters that exists in many flexible active matter, like worms or {dogs}, when they flock to the same single source.

Flocking (texture)

Synchronizing

Active matter

Chirality

Flock

Circular motion

10.48550/arxiv.2211.05332

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Vesiculation mechanisms mediated by anisotropic proteins

Physical Review Research (2023)

Ke Xiao Chen‐Xu Wu Rui Ma

Endocytosis is an essential biological process for the trafficking of macromolecules in cells. In yeast cells, this involves the invagination of a tubular membrane and the formation of endocytic vesicles. The crescent-shaped BAR proteins are generally assumed to squeeze the tubular membrane and pinch off the vesicle. Here, we theoretically investigate how BAR proteins help drive membrane fission via generating anisotropic curvatures. We show that increasing the isotropic spontaneous curvature at a localized region on the side of a tubular membrane cannot induce membrane fission if the coating area is small. However, a tubular membrane coated with proteins that generate anisotropic curvatures are prone to experience an hourglass-shaped or tube-shaped necking process, which leads to membrane fission. In addition, we propose an experimental method to determine the type of anisotropic curvatures of a protein.

Membrane Curvature

10.1103/physrevresearch.5.023176

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Citations (1)

Endocytosis against high turgor pressure is made easier by partial coating and freely rotating base

Biophysical Journal (2021)

Rui Ma Julien Berro

Turgor pressure

Membrane Curvature

Base (topology)

10.1016/j.bpj.2021.02.033

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Citations (29)

Reply on RC2

Rui Ma

Abstract. Inaccurate parameter estimation is a significant source of uncertainty in complex terrestrial biosphere models. Model parameters may have large spatial variability, even within a vegetation type. Model uncertainty from parameters can be significantly reduced by modelâdata fusion (MDF), which, however, is difficult to implement over a large region with traditional methods due to the high computational cost. This study proposed a hybrid modeling approach that couples a terrestrial biosphere model with a data-driven machine learning method, which is able to consider both satellite information and the physical mechanisms. We developed a two-step framework to estimate the essential parameters of the revised Integrated Biosphere Simulator (IBIS) pixel by pixel using the satellite-derived leaf area index (LAI) and gross primary productivity (GPP) products as âtrue values.â The first step was to estimate the optimal parameters for each sample using a modified adaptive surrogate modeling algorithm (MASM). We applied the Gaussian process regression algorithm (GPR) as a surrogate model to learn the relationship between model parameters and errors. In our second step, we built an extreme gradient boosting (XGBoost) model between the optimized parameters and local environmental variables. The trained XGBoost model was then used to predict optimal parameters spatially across the deciduous forests in the eastern United States. The results showed that the parameters were highly variable spatially and quite different from the default values over forests, and the simulation errors of the GPP and LAI could be markedly reduced with the optimized parameters. The effectiveness of the optimized model in estimating GPP, ecosystem respiration (ER), and net ecosystem exchange (NEE) were also tested through site validation. The optimized model reduced the root mean square error (RMSE) from 7.03 to 6.22âgCâm^â2âd^â1 for GPP, 2.65 to 2.11âgCâm^â2âd^â1 for ER, and 4.45 to 4.38âgCâm^â2âd^â1 for NEE. The mean annual GPP, ER, and NEE of the region from 2000 to 2019 were 5.79, 4.60, and â1.19âPgâyr^â1, respectively. The strategy used in this study requires only a few hundred model runs to calibrate regional parameters and is readily applicable to other complex terrestrial biosphere models with different spatial resolutions. Our study also emphasizes the necessity of pixel-level parameter calibration and the value of remote sensing products for per-pixel parameter optimization.

10.5194/gmd-2022-96-ac5

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Reply on RC2

Rui Ma

Abstract. Inaccurate parameter estimation is a significant source of uncertainty in complex terrestrial biosphere models. Model parameters may have large spatial variability, even within a vegetation type. Model uncertainty from parameters can be significantly reduced by modelâdata fusion (MDF), which, however, is difficult to implement over a large region with traditional methods due to the high computational cost. This study proposed a hybrid modeling approach that couples a terrestrial biosphere model with a data-driven machine learning method, which is able to consider both satellite information and the physical mechanisms. We developed a two-step framework to estimate the essential parameters of the revised Integrated Biosphere Simulator (IBIS) pixel by pixel using the satellite-derived leaf area index (LAI) and gross primary productivity (GPP) products as âtrue values.â The first step was to estimate the optimal parameters for each sample using a modified adaptive surrogate modeling algorithm (MASM). We applied the Gaussian process regression algorithm (GPR) as a surrogate model to learn the relationship between model parameters and errors. In our second step, we built an extreme gradient boosting (XGBoost) model between the optimized parameters and local environmental variables. The trained XGBoost model was then used to predict optimal parameters spatially across the deciduous forests in the eastern United States. The results showed that the parameters were highly variable spatially and quite different from the default values over forests, and the simulation errors of the GPP and LAI could be markedly reduced with the optimized parameters. The effectiveness of the optimized model in estimating GPP, ecosystem respiration (ER), and net ecosystem exchange (NEE) were also tested through site validation. The optimized model reduced the root mean square error (RMSE) from 7.03 to 6.22âgCâm^â2âd^â1 for GPP, 2.65 to 2.11âgCâm^â2âd^â1 for ER, and 4.45 to 4.38âgCâm^â2âd^â1 for NEE. The mean annual GPP, ER, and NEE of the region from 2000 to 2019 were 5.79, 4.60, and â1.19âPgâyr^â1, respectively. The strategy used in this study requires only a few hundred model runs to calibrate regional parameters and is readily applicable to other complex terrestrial biosphere models with different spatial resolutions. Our study also emphasizes the necessity of pixel-level parameter calibration and the value of remote sensing products for per-pixel parameter optimization.

Boosting

10.5194/gmd-2022-96-ac4

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Landsat-observed changes in forest cover and attribution analysis over Northern China from 1996‒2020

GIScience & Remote Sensing (2024)

Xiaobang Liu Shunlin Liang Han Ma Bing Li Yufang Zhang

Forest dynamics provide important information on the ecological environment. The Three-North Shelter Forest Program (TNSFP) is one of the world's largest reforestation/afforestation programs, however the actual changes in forest cover in the Three-North Regions (TNR) of China resulting from this program are highly uncertain. This study quantified changes in fractional forest cover (FFC) at 30 m using Landsat data from 1996 to 2020. Using the Google Earth Engine platform, more than 40,000 images from Landsat-5, Landsat 7 and Landsat-8 were integrated, and the annual surface reflectance was normalized based on the multi-band least squares regression and maximum normalized difference vegetation index composite method. An ensemble learning model trained using high-resolution Gao-Fen 2 satellite imagery was used to generate the FFC long time-series product. FFC showed an increasing trend with average rates of 0.022/10a in the last 25 years, and 0.03/10a after 2010 largely corresponding to the fourth and fifth phases of the TNSFP. There are significant regional differences in the relationship between FFC and air temperature (R2 = 0.37) and precipitation (R2 = 0.49). The increased air temperature in arid and less rainy areas inhibit the FFC increase, whereas the increase in precipitation had a promoting effect. FFC appeared more sensitive to changes in solar radiation and heat conditions in humid and rainy areas. The attribution analysis revealed that 34% of FFC changes were caused by climatic variables and 66% were caused by non-climatic factors. Among them, afforestation associated with the TNSFP significantly increased FFC, and forest fire is a key factor of forest change in the Greater Khingan Ranges and Lesser Khingan Ranges regions. Planting single tree species caused biological disasters in forests of Xinjiang and Inner Mongolia. Further analysis of the increased FFC using high-level satellite products demonstrated an improvement in environmental conditions with cooler land surface temperature and higher vegetation gross primary production over the TNR.

Afforestation

Reforestation

Land Cover

Forest cover

10.1080/15481603.2023.2300214

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Effects of arm number on the properties of transparent elastomers prepared from styrene–butadiene block copolymer

Journal of Applied Polymer Science (2006)

Rui Ma Zhenjun Zhang Lina Zhang

Abstract A series of the SBS transparent elastomers were prepared from star‐shaped SBS having different arm number by solution‐casting. Their structure and physical properties were characterized by scanning electron microscope, ultraviolet spectrometer, wide‐angle X‐ray diffractometer, differential scanning calorimetry, dynamic mechanical thermal analysis, tensile testing, and contact angle measurements. The results revealed that the miscibility, optical transparence ( T r ), tensile strength (σ b ), elongation at break (ε b ), and elasticity at low temperature of the star SBS increased with an increase of arm number. The six‐arm SBS having relatively high molecular weight exhibited a simultaneous enhancement of T r (90% at 800 nm), σ b (6.0 Mpa), and ε b (1260%). This indicated that the SBS materials having six arms had higher transparence and elasticity than others. Moreover, the water contact angle on surface of the star‐shaped SBS film increased with an increase of arm number that is enhancement of hydrophobicity. Therefore, the relatively high arm number and molecular weight played an important role in the improvement of the miscibility and properties of the SBS sheets as a result of the compacted architecture of the hyperbranched molecules. This work provides a convenient way to obtain materials with both high transparence and elasticity by increasing the arm number. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 102: 729–736, 2006

Miscibility

Thermoplastic elastomer

Diffractometer

10.1002/app.24307

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Scalable and Selective Electrochemical Hydrogenation of Polycyclic Arenes

Angewandte Chemie (2024)

Hao Li Yan Li Jiaye Chen Lijun Lu Pengjie Wang

Abstract The reduction of aromatic compounds constitutes a fundamental and ongoing area of investigation. The selective reduction of polycyclic aromatic compounds to give either fully or partially reduced products remains a challenge, especially in applications to complex molecules at scale. Herein, we present a selective electrochemical hydrogenation of polycyclic arenes conducted under mild conditions. A noteworthy achievement of this approach is the ability to finely control both the complete and partial reduction of specific aromatic rings within polycyclic arenes by judiciously varying the reaction solvents. Mechanistic investigations elucidate the pivotal role played by in situ proton generation and interface regulation in governing reaction selectivity. The reductive electrochemical conditions show a very high level of functional‐group tolerance. Furthermore, this methodology represents an easily scalable reduction (demonstrated by the reduction of 1 kg scale starting material) using electrochemical flow chemistry to give key intermediates for the synthesis of specific drugs.

10.1002/ange.202407392

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Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data

Geoscientific model development (2022)

Rui Ma Jingfeng Xiao Shunlin Liang Han Ma Tao He

Abstract. Inaccurate parameter estimation is a significant source of uncertainty in complex terrestrial biosphere models. Model parameters may have large spatial variability, even within a vegetation type. Model uncertainty from parameters can be significantly reduced by model–data fusion (MDF), which, however, is difficult to implement over a large region with traditional methods due to the high computational cost. This study proposed a hybrid modeling approach that couples a terrestrial biosphere model with a data-driven machine learning method, which is able to consider both satellite information and the physical mechanisms. We developed a two-step framework to estimate the essential parameters of the revised Integrated Biosphere Simulator (IBIS) pixel by pixel using the satellite-derived leaf area index (LAI) and gross primary productivity (GPP) products as “true values.” The first step was to estimate the optimal parameters for each sample using a modified adaptive surrogate modeling algorithm (MASM). We applied the Gaussian process regression algorithm (GPR) as a surrogate model to learn the relationship between model parameters and errors. In our second step, we built an extreme gradient boosting (XGBoost) model between the optimized parameters and local environmental variables. The trained XGBoost model was then used to predict optimal parameters spatially across the deciduous forests in the eastern United States. The results showed that the parameters were highly variable spatially and quite different from the default values over forests, and the simulation errors of the GPP and LAI could be markedly reduced with the optimized parameters. The effectiveness of the optimized model in estimating GPP, ecosystem respiration (ER), and net ecosystem exchange (NEE) were also tested through site validation. The optimized model reduced the root mean square error (RMSE) from 7.03 to 6.22 gC m−2 d−1 for GPP, 2.65 to 2.11 gC m−2 d−1 for ER, and 4.45 to 4.38 gC m−2 d−1 for NEE. The mean annual GPP, ER, and NEE of the region from 2000 to 2019 were 5.79, 4.60, and −1.19 Pg yr−1, respectively. The strategy used in this study requires only a few hundred model runs to calibrate regional parameters and is readily applicable to other complex terrestrial biosphere models with different spatial resolutions. Our study also emphasizes the necessity of pixel-level parameter calibration and the value of remote sensing products for per-pixel parameter optimization.

Sensor Fusion

10.5194/gmd-15-6637-2022

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Citations (32)

Spontaneous stable rotation of flocking flexible active matter

Soft Matter (2024)

Gaoxiao Jiang Zhihong You Rui Ma Chen‐Xu Wu

In this paper we present an

Flocking (texture)

Active matter

10.1039/d4sm00372a

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