Entropic Elasticity of Polymers and Their Networks

The elastic energy for many biopolymer systems is comparable to the thermal energy at room temperature. Therefore, biopolymers and their networks are constantly under thermal fluctuations. From the point of view of thermodynamics, this suggests that entropy plays a crucial role in determining the mechanical behaviors of these filamentous biopolymers. One of the main goals of this thesis is to understand how thermal fluctuations affect the mechanical properties and behaviors of filamentous networks, and also how stress affects the thermal fluctuations. Filaments and filamentous networks are viewed as mechanical structures, whose static equilibrium states under the action of loads or kinematic constraints are determined in the first step of the investigation. Typically, a system is discretized and represented by a finite set of kinematic variables that characterizes the configuration space. In the next step, we apply statistical mechanics to study the thermo-mechanical properties of the system. We approximate the local minimum energy well to quadratic order. Such a quadratic approximation for a discrete system gives rise to a stiffness matrix that characterizes the flexibility of the system around the ground state. Using the multidimensional Gaussian integral technique, the partition function is efficiently evaluated, provided that the energy well around the ground state is steep. In this case, the dominant contribution to the partition function is from the states that are close to the equilibrium state, whose energies are well approximated by the quadratic energy expression. All thermodynamic properties of the system can be further evaluated from the partition function. Fluctuation of the system, in particular, scales linearly with the temperature and inversely with the stiffness matrix. Therefore, the stiffness matrix governs the statistical mechanical behavior of the system near its ground state. We also show that a system with constraints on its kinematic variables can be converted into an effective non-constrained system. Using the above theoretical framework, we study the thermo-mechanical properties of filaments and filamentous networks under different loadings and confinement conditions. The filaments need not be homogeneous in the mechanical properties, and they can be subjected to non-uniform distributed loads or non-uniform confinements. Under compression, a filament can buckle. Buckling in a filament network can reduce the stiffness of the structure, which leads to significant thermal fluctuations around the buckling point. Properties of a triangular network under pure expansion, simple shear and uniaxial tension are also investigated in this thesis. As further applications, we discuss the protein forced unfolding problem. We show that different unfolding behaviors of a protein chain can be understood using a system of three equations. We also discuss the internal fluctuations of DNA under confinement and show a length-dependent transition between the de Gennes and Odijk regimes. We also show that entropy plays a role in driving the motion of a piece of DNA along a non-uniform channel. We derive the entropic force on the DNA in this thesis and discuss the coupled migration and deformation of the polymer under non-uniform confinement

Entropically Driven Motion of Polymers in Nonuniform Nanochannels

In nanofluidic devices, nonuniform confinement induces an entropic force that automatically drives biopolymers toward less-confined regions to gain entropy. To understand this phenomenon, we first analyze the diffusion of an entropy-driven particle system. The derived Fokker-Planck equation reveals an effective driving force as the negative gradient of the free energy. The derivation also shows that both the diffusion constant and drag coefficient are location dependent on an arbitrary free-energy landscape. As an application, DNA motion and deformation in nonuniform channels are investigated. Typical solutions reveal large gradients of stress on the polymer where the channel width changes rapidly. Migration of DNA in several nonuniform channels is discussed

Structural Transition from Helices to Hemihelices

Helices are amongst the most common structures in nature and in some cases, such as tethered plant tendrils, a more complex but related shape, the hemihelix forms. In its simplest form it consists of two helices of opposite chirality joined by a perversion. A recent, simple experiment using elastomer strips reveals that hemihelices with multiple reversals of chirality can also occur, a richness not anticipated by existing analyses. Here, we show through analysis and experiments that the transition from a helical to a hemihelical shape, as well as the number of perversions, depends on the height to width ratio of the strip's cross-section. Our findings provides the basis for the deterministic manufacture of a variety of complex three-dimensional shapes from flat strips

Differential Contributions of Conformation Extension and Domain Unfolding to Properties of Fibronectin Nanotextiles

Fibronectin (FN) textiles are built as nanometer-thick fabrics. When uniaxially loaded, these fabrics exhibit a distinct threshold between elastic and plastic deformation with increasing stretch. Fabric mechanics are modeled using an eight-chain network and two-state model, revealing that elastic properties of FN depend on conformational extension of the protein and that plastic deformation depends on domain unfolding. Our results suggest how the molecular architecture of a molecule can be exploited for designer mechanical properties of a bulk material.Engineering and Applied Science

State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter

Crop models play a paramount role in providing quantitative information on crop growth and field management. However, its prediction performance degrades significantly in the presence of unknown, uncertain parameters and noisy measurements. Consequently, simultaneous state and parameter estimation (SSPE) for crop model is required to maximize its potentials. This work aims to develop an integrated dynamic SSPE framework for the AquaCrop model by leveraging constrained particle filter, crop sensitivity analysis and UAV remote sensing. Both Monte Carlo simulation and one winter wheat experimental case study are performed to validate the proposed framework. It is shown that: (i) the proposed framework with state/parameter bound and parameter sensitivity information outperforms conventional particle filter and constrained particle filter in both state and parameter estimation in Monte Carlo simulations; (ii) in real-world experiment, the proposed approach achieves the smallest root mean squared error for canopy cover estimation among the three algorithms by using day forward-chaining validation method

Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier

Land cover classification is able to reflect the potential natural and social process in urban development, providing vital information to stakeholders. Recent solutions on land cover classification are generally addressed by remotely sensed imagery and supervised classification methods. However, a high-performance classifier is desirable but challenging due to the existence of model hyperparameters. Conventional approaches generally rely on manual tuning, which is time-consuming and far from satisfying. Therefore, this work aims to propose a systematic method to automatically tune the hyperparameters by Bayesian parameter optimization for the random forest classifier. The recently launched Sentinel-2A/B satellites are drawn to provide the remote sensing imageries for land cover classification case study in Beijing, China, which have the best spectral/spatial resolutions among the freely available satellites. The improved random forest with Bayesian parameter optimization is compared against the support vector machine (SVM) and random forest (RF) with default hyperparameters by discriminating five land cover classes including building, tree, road, water and crop field. Comparative experimental results show that the optimized RF classifier outperforms the conventional SVM and the RF with default hyperparameters in terms of accuracy, precision and recall. The effects of band/feature number and the band usefulness are also assessed. It is envisaged that the improved classifier for Sentinel-2 satellite image processing can find a wide range of applications where high-resolution satellite imagery classification is applicable

circFBXW7 attenuates malignant progression in lung adenocarcinoma by sponging miR-942-5p

Background: As a type of non-coding RNA, circular RNAs (circRNAs) are considered to be functional molecules associated with human cancers. An increasing number of circRNAs have been verified in malignant progression in a number of cancers. The circRNA, circFBXW7, has been proven to play an important role in tumor proliferation and metastasis. However, whether circFBXW7 influences progression in lung adenocarcinoma (LUAD) remains unclear. Methods: Quantitative real-time reverse transcriptase PCR (qRT-PCR) was used to verify circFBXW7 in LUAD cell lines and LUAD tissues. Kaplan-Meier analysis was then used to compare the disease-free survival (DFS) and overall survival (OS) of these LUAD patients. The biological function of circFBXW7 was examined by overexpression and knockdown of circFBXW7 using MTT assay, EdU assay, wound-healing assay, and Transwell in vitro assays. To explore the mechanism of the circFBXW7, RNA pull-down assay, dual luciferase reporter assay, and RNA immunoprecipitation (RIP) assay were employed to examine the interaction between circFBXW7 and miR-942-5p. Western blot was used to study the fundamental proteins associated with the epithelial-mesenchymal transition (EMT) pathway. In vivo studies with BALB/c nude mice subcutaneously injected with cells stably overexpressing circFBXW7 were performed to further validate the in vitro results. Results: circFBXW7 was downregulated in LUAD cell lines and tissues, and LUAD patients with lower levels had shorter DFS and OS. The in vitro study showed that circFBXW7 overexpression inhibited proliferation and migration of A549 and HCC2279 cell lines. These results were confirmed by circFBXW7 knockdown, which showed the reverse effect. The in vivo model showed that the circRNA levels influenced the tumor growth. Finally, we determined that circFBXW7 target miRNA-942-5p which regulates the EMT gene BARX2. The modulation of circFBXW7 levels produced significant changes in EMT genes in vitro and in vivo. Conclusions: Our findings showed that circFBXW7 inhibits proliferation and migration by controlling the miR-942-5p/BARX2 axis in LUAD cell lines and its levels correlates with patient survival suggesting that regulating circFBXW7 could have therapeutic value in treating LUAD patients

Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery

Crop disease is widely considered as one of the most pressing challenges for food crops, and therefore an accurate crop disease detection algorithm is highly desirable for its sustainable management. The recent use of remote sensing and deep learning is drawing increasing research interests in wheat yellow rust disease detection. However, current solutions on yellow rust detection are generally addressed by RGB images and the basic semantic segmentation algorithms (e.g., UNet), which do not consider the irregular and blurred boundary problems of yellow rust area therein, restricting the disease segmentation performance. Therefore, this work aims to develop an automatic yellow rust disease detection algorithm to cope with these boundary problems. An improved algorithm entitled Ir-UNet by embedding irregular encoder module (IEM), irregular decoder module (IDM) and content-aware channel re-weight module (CCRM) is proposed and compared against the basic UNet while with various input features. The recently collected dataset by DJI M100 UAV equipped with RedEdge multispectral camera is used to evaluate the algorithm performance. Comparative results show that the Ir-UNet with five raw bands outperforms the basic UNet, achieving the highest overall accuracy (OA) score (97.13%) among various inputs. Moreover, the use of three selected bands, Red-NIR-RE, in the proposed Ir-UNet can obtain a comparable result (OA: 96.83%) while with fewer spectral bands and less computation load. It is anticipated that this study by seamlessly integrating the Ir-UNet network and UAV multispectral images can pave the way for automated yellow rust detection at farmland scales

Bayesian Calibration of AquaCrop Model for Winter Wheat by Assimilating UAV Multi-Spectral Images

Crop growth model plays a paramount role in smart farming management, which not only provides quantitative information on crop development but also evaluates various management strategies. A reliable model is desirable but challenging due to the presence of unknown and uncertain parameters; therefore, crop model calibration is significant to achieve its potentials. This work is focused on the calibration of AquaCrop model by leveraging advanced Bayesian inference algorithms and UAV multi-spectral images at field scales. In particular, aerial images with high spatial- temporal resolutions are first applied to obtain Canopy Cover (CC) value by using machine learning based classification. The CC is then assimilated into AquaCrop model and uncertain parameters could be inferred by Markov Chain Monte Carlo (MCMC). Both simulation and experimental validation are performed. The experimental aerial images of winter wheat at Yangling district from Oct/2017 to June/2018 are applied to validate the proposed method against the conventional optimisation based approach by Simulated Annealing (SA). 100 Monte Carlo simulations show that the root mean squared error (RMSE) of Bayesian approach yields a smaller parameter estimation error than optimisation approach. While the experimental results show that: (i) a good wheat/background classification result is obtained for the accurate calculation of CC; (ii) the predicted CC values by Bayesian approach are consistent with measurements by 4-fold cross validation, where the RMSE is 0.0271 smaller than optimisation approach (0.0514); (iii) in addition to parameter estimation, their distribution information is also obtained in the developed Bayesian approach, reflecting the prediction confidence. It is believed that the Bayesian model calibration, although is developed for AquaCrop model, can find a wide range of applications to various simulation models in agriculture and forestry