High resolution quantification of cellular forces for rigidity sensing

This thesis describes a comprehensive study of understanding the mechanism of rigidity sensing by quantitative analysis using submicron pillar array substrates. From mechanobiology perspective, we explore and study molecular pathways involved in rigidity and force sensing at cell-matrix adhesions with regard to cancer, regeneration, and development by quantification methods. In Chapter 2 and 3, we developed fabrication and imaging techniques to enhance the performance of a submicron pillar device in terms of spatial and temporal measurement ability, and we discovered a correlation of rigidity sensing forces and corresponding proteins involved in the early rigidity sensing events. In Chapter 2, we introduced optical effect arising from submicron structure imaging, and we described a technique to identify the correct focal plane of pillar tip by fabricating a substrate with designed-offset pillars. From calibration result, we identified the correct focal plane that was previously overlooked, and verified our findings by other imaging techniques. In Chapter 3, we described several techniques to selectively functionalize elastomeric pillars top and compared these techniques in terms of purposes and fabrication complexity. Techniques introduced in this chapter included direct labeling, such as stamping of fluorescent substances (organic dye, nano-diamond, q-dot) to pillars top, as well as indirect labeling that selectively modify the surface of molds with either metal or fluorescent substances. In Chapter 4, we examined the characteristics of local contractility forces and identified the components formed a sarcomere like contractile unit (CU) that cells use to sense rigidity. CUs were found to be assembled at cell edge, contain myosin II, α-actinin, tropomodulin and tropomyosin (Tm), and resemble sarcomeres in size (~2 μm) and function. Then we performed quantitative analysis of CUs to evaluate rigidity sensing activity over ~8 hours time course and found that density of CUs decrease with time after spreading on stiff substrate. However addition of EGF dramatically increased local contraction activity such that about 30% of the total contractility was in the contraction units. This stimulatory effect was only observed on stiff substrate not on soft. Moreover, we find that in the early interactions of cells with rigid substrates that EGFR activity is needed for normal spreading and the assembly of local contraction units in media lacking serum and any soluble EGF. In Chapter 5, we performed high temporal- and spatial-resolution tracking of contractile forces exerted by cells on sub-micron elastomeric pillars. We found that actomyosin-based sarcomere-like CUs simultaneously moved opposing pillars in net steps of ~2.5 nm, independent of rigidity. What correlated with rigidity was the number of steps taken to reach a force level that activated recruitment of α-actinin to the CUs. When we removed actomyosin restriction by depleting tropomyosin 2.1, we observed larger steps and higher forces that resulted in aberrant rigidity sensing and growth of non-transformed cells on soft matrices. Thus, we conclude that tropomyosin 2.1 acts as a suppressor of growth on soft matrices by supporting proper rigidity sensing

The Application of Wavelet Neural Network in the Settlement Monitoring of Subway

The settlement monitoring of subway runs through the entire construction stage of subway. It is very important to predict the accurate settlement value for construction safety of subway. In this paper, the wavelet transform is used to denoise the settlement data. The auxiliary wavelet neural network, embedded wavelet neural network and single BP neural network are applied to predict the settlement of Tianjin subway. Compared with single BP neural network and auxiliary wavelet neural network, the embedded wavelet neural network model has a higher accuracy and better prediction effect. The embedded wavelet neural network is more valuable than the BP neural network model so it can be used in the prediction of subway settlemen

Long-Term Nitrogen Fertilization Elevates the Activity and Abundance of Nitrifying and Denitrifying Microbial Communities in an Upland Soil: Implications for Nitrogen Loss From Intensive Agricultural Systems

The continuous use of nitrogen (N) fertilizers to increase soil fertility and crop productivity often results in unexpected environmental effects and N losses through biological processes, such as nitrification and denitrification. In this study, multidisciplinary approaches were employed to assess the effects of N fertilization in a long-term (~20 years) field experiment in which a fertilizer gradient (0, 200, 400, and 600 kg N ha−1 yr−1) was applied in a winter wheat-summer maize rotation cropping system in the North China Plain, one of the most intensive agricultural regions in China. The potential nitrification/denitrification rates, bacterial community structure, and abundances of functional microbial communities involved in key processes of the N cycle were assessed during both the summer maize (SM) and winter wheat (WW) seasons. Long-term N fertilization resulted in a decrease in soil pH and an increase in soil organic matter (OM), total N and total carbon concentrations. Potential nitrification/denitrification and the abundances of corresponding functional N cycling genes were positively correlated with the fertilization intensity. High-throughput sequencing of the 16S rRNA gene revealed that the increased fertilization intensity caused a significant decrease of bacterial diversity in SM season, while changed the microbial community composition such as increasing the Bacteroidetes abundance and decreasing Acidobacteria abundance in both SM and WW seasons. The alteration of soil properties markedly correlated with the variation in microbial structure, as soil pH and OM were the most predominant factors affecting the microbial structure in the SM and WW seasons, respectively. Furthermore, consistently with the results of functional gene quantification, functional prediction of microbial communities based on 16S rRNA sequence data also revealed that the abundances of the key nitrificaiton/denitrification groups were elevated by long-term N inputs. Taken together, our results suggested that soil microbial community shifted consistently in both SM and WW seasons toward a higher proportion of N-cycle microbes and exhibited higher N turnover activities in response to long-term elevated N fertilizer. These findings provided new insights into the molecular mechanisms responsible for N loss in intensively N fertilized agricultural ecosystems

Radio frequency electrical transduction of graphene mechanical resonators

We report radio frequency (rf) electrical readout of graphene mechanical resonators. The mechanical motion is actuated and detected directly by using a vector network analyzer, employing a local gate to minimize parasitic capacitance. A resist-free doubly clamped sample with resonant frequency ~ 34 MHz, quality factor ~ 10000 at 77 K, and signal-to-background ratio of over 20 dB is demonstrated. In addition to being over two orders of magnitude faster than the electrical rf mixing method, this technique paves the way for use of graphene in rf devices such as filters and oscillators.Comment: 10 pages, 2 figure

Tropomyosin controls sarcomere-like contractions for rigidity sensing and suppressing growth on soft matrices

Cells test the rigidity of the extracellular matrix by applying forces to it through integrin adhesions. Recent measurements show that these forces are applied by local micrometre-scale contractions, but how contraction force is regulated by rigidity is unknown. Here we performed high temporal- and spatial-resolution tracking of contractile forces by plating cells on sub-micrometre elastomeric pillars. We found that actomyosin-based sarcomere-like contractile units (CUs) simultaneously moved opposing pillars in net steps of ∼2.5 nm, independent of rigidity. What correlated with rigidity was the number of steps taken to reach a force level that activated recruitment of α-actinin to the CUs. When we removed actomyosin restriction by depleting tropomyosin 2.1, we observed larger steps and higher forces that resulted in aberrant rigidity sensing and growth of non-transformed cells on soft matrices. Thus, we conclude that tropomyosin 2.1 acts as a suppressor of growth on soft matrices by supporting proper rigidity sensing

Tropomyosin controls sarcomere-like contractions for rigidity sensing and suppressing growth on soft matrices

Cells test the rigidity of the extracellular matrix by applying forces to it through integrin adhesions. Recent measurements show that these forces are applied via local micrometre-scale contractions, but how contraction force is regulated by rigidity is unknown. Here we performed high temporal- and spatial-resolution tracking of contractile forces by plating cells on sub-micron elastomeric pillars. We found that actomyosin-based sarcomere-like contractile units (CUs) simultaneously moved opposing pillars in net steps of ~2.5 nm, independent of rigidity. What correlated with rigidity was the number of steps taken to reach a force level that activated recruitment of α-actinin to the CUs. When we removed actomyosin restriction by depleting tropomyosin 2.1, we observed larger steps and higher forces that resulted in aberrant rigidity sensing and growth of non-transformed cells on soft matrices. Thus, we conclude that tropomyosin 2.1 acts as a suppressor of growth on soft matrices by supporting proper rigidity sensing

Response of Nitrifier and Denitrifier Abundance and Microbial Community Structure to Experimental Warming in an Agricultural Ecosystem

Soil microbial community plays an important role in terrestrial carbon and nitrogen cycling. However, the response of the soil nitrifier and denitrifier communities to climate warming is poorly understood. A long-term field warming experiment has been conducted for 8 years at Luancheng Experimental Farm Station on the North China Plain; we used this field to examine how soil microbial community structure, nitrifier, and denitrifier abundance respond to warming under regular irrigation (RI) and high irrigation (HI) at different soil depths (0–5, 5–10, and 10–20 cm). Nitrifier, denitrifier, and the total bacterial abundance were assessed by quantitative polymerase chain reaction of the functional genes and 16S rRNA gene, respectively. Bacterial community structure was studied through high throughput sequencing of the 16S rRNA gene. Under RI, warming significantly (P < 0.05) increased the potential nitrification rate and nitrate concentration and decreased the soil moisture. In most of the samples, warming increased the ammonia-oxidizing bacteria abundance but decreased the ammonia-oxidizing archaea (AOA) and denitrifier (nirK, nirS, and nosZ genes) abundance. Under HI, there was a highly increased AOA and 16S rRNA gene abundance and a slightly higher denitrifier abundance compared with RI. Warming decreased the bacterial diversity and species richness, and the microbial community structure differed greatly between the warmed and control plots. The decrease in bacterial diversity was higher in RI than HI and at the 0–5 cm depths than at the 5–10 and 10–20 cm soil depths. Warming led to an increase in the relative abundance of Actinobacteria, Bacteroidetes, and TM7 but a decrease in Acidobacteria, Alphaproteobacteria, Deltaproteobacteria, Nitrospira, and Planctomycetes. The greater shift in microbial community structure was observed only in RI at the 0–5 cm soil depth. This study provides new insight into our understanding of the nitrifier and denitrifier activity and microbial community response to climate warming in agricultural ecosystems

Bayesian variance changepoint detection in linear models with symmetric heavy-tailed errors

Normality and static variance are very common assumptions in traditional financial theories and risk modeling for mathematical convenience. Empirical evidence suggests otherwise. With the rapid growth in volatility-based financial innovations and market, it is beneficial and essential to look beyond the traditional restrictive assumptions. This paper discusses Bayesian analysis of the variance changepoints problem in linear models with flexible error distributions. Specifically, we consider the class of scale mixtures of normal distributions, which not only exhibits symmetric heavy-tailed behavior, but also includes many common error distributions as special cases, such as the normal and Student-t distributions. Our proposed approach can reduce the influence of atypical observations and thus offer a robust technique for detecting the variance changepoints in many financial and economic data. We propose an efficient Gibbs sampling procedure to generate posterior samples and in turn to perform Bayesian inference. Simulation studies are conducted to demonstrate satisfactory performance of the proposed methodology. The closing price data set from the US stocks database is analyzed for illustrative purposes