Search

There as an urgent need to quantify the endothelial wound-healing process in response to fluid shear stress to improve the biological and clinical understanding of healing mechanisms, which is of great importance for preventing healing impairment, chronic wounds, and postoperative in-stent restenosis. However, current experimental platforms not only require expensive, cumbersome, and powered pumping devices (to, e.g., generate cell scratches and load shear stress stimulation) but also lack quantitative controls for quantitative analysis. In this paper, a passive pump-assisted microfluidic assay is developed to quantify endothelial wound healing in response to fluid shear stress. Our assay consists of passive constant-flow pumps based on the siphon principle and a three-inlet microfluidic chip for cell wound-healing experiments. We also propose a method for quantitatively adjusting cell scratch size by controlling trypsin flow. Both numerical simulations and fluorescein experiments validate the effectiveness of this method. Moreover, we use the designed microfluidic assay to successfully generate cell scratches, load a 12-h shear stress of 5 dyn/cm

The mechanical properties of single cells have been recognized as biomarkers for identifying individual cells and diagnosing human diseases. Microfluidic devices based on the flow cytometry principle, which are not limited by the vision field of a microscope and can achieve a very high throughput, have been extensively adopted to measure the mechanical properties of single cells. However, these kinds of microfluidic devices usually required pressure-driven pumps with a very low flow rate and high precision. In this study, we developed a high-throughput microfluidic device inspired by the Wheatstone bridge principle for characterizing the mechanical properties of single cells. The microfluidic analogue of the Wheatstone bridge not only took advantage of flow cytometry, but also allowed precise control of a very low flow rate through the constricted channel with a higher input flow rate generated by a commercially available pressure-driven pump. Under different input flow rates of the pump, the apparent elastic moduli and the fluidity of osteosarcoma (U-2OS) cells and cervical carcinoma (HeLa) cells were measured by monitoring their dynamic deformations passing through the bridge-channel with different sizes of rectangular constrictions. The results showed that the input flow rate had little effect on measuring the mechanical properties of the cells, while the ratio of cell radius to effective constriction radius was different

Accurate signal transmission between neurons is accomplished by vesicle release with high spatiotemporal resolution in the central nervous system. The vesicle release occurs mainly in the active zone (AZ), a unique area on the presynaptic membrane. Many structural proteins expressed in the AZ connect with other proteins nearby. They can also regulate the precise release of vesicles through protein-protein interactions. RIM-binding proteins (RIM-BPs) are one of the essential proteins in the AZ. This review summarizes the structures and functions of three subtypes of RIM-BPs, including the interaction between RIM-BPs and other proteins such as Bassoon and voltage-gated calcium channel, their significance in stabilizing the AZ structure in the presynaptic region and collecting ion channels, and ultimately regulating the fusion and release of neuronal vesicles.

The morbidity rate of ulcerative colitis (UC) in the world is increasing year by year, recurrent episodes of diarrhea, mucopurulent and bloody stools, and abdominal pain are the main symptoms, reducing the quality of life of the patient and affecting the productivity of the society. In this study, we sought to develop robust diagnostic biomarkers for UC, to uncover potential targets for anti-TNF-ɑ drugs, and to investigate their associated pathway mechanisms. We collected single-cell expression profile data from 9 UC or healthy samples and performed cell annotation and cell communication analysis. Revealing the possible pathogenesis of ulcerative colitis by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis. Based on the disease-related modules obtained from weighted correlation network analysis (WGCNA) analysis, we used Lasso regression analysis and random forest algorithm to identify the genes with the greatest impact on disease (EPB41L3, HSD17B3, NDRG1, PDIA5, TRPV3) and further validated the diagnostic value of the model genes by various means. To further explore the relationship and mechanism between model genes and drug sensitivity, we collected gene expression profiles of 185 UC patients before receiving anti-tumor necrosis factor drugs, and we performed functional analysis based on the results of differential analysis between NR tissues and R tissues, and used single-sample GSEA (ssGSEA) and CIBERSORT algorithms to explore the important role of immune microenvironment on drug sensitivity. The results suggest that our model is not only helpful in aiding diagnosis, but also has implications for predicting drug efficacy; in addition, model genes may influence drug sensitivity by affecting immune cells. We suggest that this study has developed a diagnostic model with higher specificity and sensitivity, and also provides suggestions for clinical administration and drug efficacy prediction.

The requirement for transit reliability grows with the increase of pace of life since unstable bus arrivals can raise the anxiety of waiting passengers. This paper proposes a reliability assessment method to evaluate the reliability of each bus stop on the route and the reliability of bus routes. In reliability prediction, the prediction target is locked by rolling horizon to reduce the interference of other information. In addition, a prediction method of the reliability of further transit service using the accurate online support vector machine is proposed. This prediction can provide more accurate and stable data for the arrival of buses and reduce unnecessary waiting of passengers. Finally, the reliability prediction method proposed is tested with the real data of a bus route in Dalian, China. The results show that the accurate online support vector machine with reasonable parameters can predict the reliability of transit service accurately.

Vehicle electrification can help to decrease environmental pollution. But fast electricity consumption and inconvenient recharging service restrict the widespread adoption of electric vehic...

Explosive increase of industrial data collected from sensors has brought increasing attractions to the data-driven predictive maintenance for industrial machines in cyber-physical systems (CPSs). Since machinery faults are always caused by performance deterioration of components, learning the deteriorating mode from observed sensor data facilitates the prognostics of impeding faults and predicting the remaining useful life (RUL). In modern CPSs, several key performance indicators (KPIs) are monitored to detect the corresponding fine-grained deteriorating modes of industrial machines. However, the overall deterioration estimation and RUL prediction based on these KPIs with various patterns have been a great challenge, especially without labels of deteriorating index or uninterpretable of root causes. In this paper, we proposed K-PdM , a cluster-based hidden Markov model for the machinery deterioration estimation and RUL prediction based on multiple KPIs. The method uncovers the fine-grained deteriorating modes of machines through each unlabeled KPI data and learns a mapping between each deteriorating KPI index and RULs. Accordingly, an overall deterioration estimation and RUL prediction of machine are able to be achieved based on the combination of each KPI’s deterioration estimation. Moreover, a set of interpretable semantic rules are setup to analyze the root cause of performance deterioration among KPIs. An experimental application is proposed to demonstrate its applicability based on the PHM08 data sets. The obtained results show their effectiveness to predict the RULs of machines.

To effectively predict traffic fatalities and promote the friendly development of transportation, a prediction model of traffic fatalities is established based on support vector machine (SVM). As the prediction accuracy of SVM largely depends on the selection of parameters, Particle Swarm Optimization (PSO) is introduced to find the optimal parameters. In this paper, small sample and nonlinear data are used to predict fatalities of traffic accident. Traffic accident statistics data of China from 1981 to 2012 are chosen as experimental data. The input variables for predicting accident are highway mileage, vehicle number and population size while the output variables are traffic fatality. To verify the validity of the proposed prediction method, the back-propagation neural network (BPNN) prediction model and SVM prediction model are also used to predict the traffic fatalities. The results show that compared with BPNN prediction model and SVM model, the prediction model of traffic fatalities based on PSO-SVM has higher prediction precision and smaller errors. The model can be more effective to forecast the traffic fatalities. And the method using particle swarm optimization algorithm for parameter optimization of SVM is feasible and effective. In addition, this method avoids overcomes the problem of “over learning” in neural network training progress

In order to learn the performance degradation mode of machines for diagnostics and prognostics in Cyber-Physical System (CPS), it is necessary to analyze observed sensor data to find the internal run-to-failure states of a system. In this paper, the research goal focuses on learning the internal state and transition of the degraded states from original data, which has some advantages to reveal the working dynamics of the system. Due to the existence of unlabeled data, the paper proposes an unsupervised framework based on clustering and Hidden Markov Model, named Cluster-based Hidden Markov Model (cHMM). The cHMM aims at converting raw sensory stream into a sequence of symbols as the initial observation and hidden state sequences, and then an extended Viterbi algorithm based on Hidden Markov Model (HMM) is used to discover the final stable hidden states and transitional rules in a dynamic programming way. Based on the learned model and expert's knowledge, performance degradation failure and the root-cause could be predicted and reasoned. Finally, experiments and proof-of-concept demonstration are given to validate the feasibility and effectiveness of the framework based on C-MAPSS turbofan engine dataset.

Web of Things (WoT) facilitates the discovery and interoperability of Internet of Things (IoT) devices in a cyber-physical system (CPS). Moreover, a uniform knowledge representation of physical resources is quite necessary for further composition, collaboration, and decision-making process in CPS. Though several efforts have integrated semantics with WoT, such as knowledge engineering methods based on semantic sensor networks (SSN), it still could not represent the complex relationships between devices when dynamic composition and collaboration occur, and it totally depends on manual construction of a knowledge base with low scalability. In this paper, to addresses these limitations, we propose the semantic Web of Things (SWoT) framework for CPS (SWoT4CPS). SWoT4CPS provides a hybrid solution with both ontological engineering methods by extending SSN and machine learning methods based on an entity linking (EL) model. To testify to the feasibility and performance, we demonstrate the framework by implementing a temperature anomaly diagnosis and automatic control use case in a building automation system. Evaluation results on the EL method show that linking domain knowledge to DBpedia has a relative high accuracy and the time complexity is at a tolerant level. Advantages and disadvantages of SWoT4CPS with future work are also discussed.

The study of dynamic changes and spatial variation of landscape patterns is important to deeply understand the relationship between human activities and the natural environment. We selected a typical mountain area, Shizhu County, as the study area and analyzed the landscape&rsquo, s dynamic changes and spatial variation in that area from 2000&ndash, 2015. The results showed that cropland and forestland were the dominant landscape types in the study area. Cropland and grassland areas decreased, being mainly converted to forestland. Forestland and built-up land areas were increasing, the increase in built-up land was mainly due to the invasion into cropland areas, and the increase in forestland was mainly due to the conversion of cropland and grassland. Water bodies were affected by factors such as water storage in the Three Gorges Reservoir, and their area continued to increase. The change in landscape was most dramatic from 2005&ndash, 2010, mainly due to the rapid increase in the areas of built-up land and water bodies and the rapid decrease in grassland area. There were apparent spatial variations in landscape distribution, patterns, and dynamic changes. Although water bodies were mainly distributed in the relatively gentle slope areas with an elevation of less than 200 m and a slope of 0&deg, &ndash, 6&deg, other landscapes were concentrated at an elevation higher than 500 m, a slope of 15&deg, 35&deg, with a westerly or northwesterly aspect. These areas also had the most drastic landscape changes. At the type-level and the landscape-level, landscape indices showed greater variation with elevation and slope than with aspect. Finally, the variations with elevation, slope, and aspect differed among different landscape types.

Integrating semantic technology to the Web of Things (WoT) facilitates the creation of a networked knowledge infrastructure with more interoperable data from both physical and cyber world. However, most of current solutions relies on manual methods based on domain-specific ontology, which is only suitable for domain expert and developers but not compatible with large-scale knowledge construction. This paper proposes a semi-automatic annotation framework for the metadata representation of WoT resource. This framework is based on a probabilistic graphical model to collectively infer entities, classes and relations from schematic WoT resources mapping to domain independent knowledge base. A proof-of-concept implementation and performance evaluation are illustrated to show a feasibility of our method.

We report quantitative measurement of terrace size as a function of annealing time for step bunching induced by direct current heating in the step-down direction for the temperatures 945°C and 1245°C. This result is shown to be inconsistent with simple models of step bunching in which there is a temperature-independent electromigration force on diffusing surface atoms. Deposition of Si atoms onto the surface held at 945°C with current running in the step-down direction slows the step bunching. By estimating the parameters governing step flow from experimental observations, this result is shown to be inconsistent with simple models of step bunching incorporating an electromigration force as the source of diffusional anisotropy. The evolution of step bunching was monitored by measuring the growth of the terrace sizes revealing a functional form of t α , with α ∼0.5.

Instabilities in step structure induced by direct-current heating have been measured using STM on Si(111) surfaces with an equilibrium step separation of approximately 1500 A. The two-dimensional structure of kinetically formed step bunches and associated “crossing arrays” of single-height steps shows qualitatively good agreement with predictions of a generalized theory of step-flow instability. Quantitative analysis of the structures is consistent with the theory. The result provides the first experimental determination of an effective anisotropy ratio governing kinetic step bunching. The measured value of this anisotropy ratio is 0.20 ± 0.03.

Low‐energy electron diffraction (LEED) and Auger electron spectroscopy (AES) have been used to characterize a range of silicon surfaces with orientations along the azimuth from (001) to (111). Both clean and carbon‐contaminated surfaces have been studied. When clean, no faceting of these surfaces is observed, even after prolonged annealing. Following annealing above 950 °C, however, carbon contaminated surfaces oriented between (111) and (113) facet to expose (111) and (113) surfaces. Carbon‐contaminated singular and vicinal Si(001) surfaces facet to expose four {113} facets. The clean Si(113) surface is reconstructed: the same reconstruction also appears on the {113} regions of the faceted surfaces. The (111) regions of the faceted surfaces also show the clean‐surface 7×7 reconstruction. The faceting can be induced by as little as 0.3% of carbon on Si(112) but 3% carbon is required before faceting is observable on Si(113). Optical microscope images of the surfaces reveal pits up to several microns in width, with well‐defined shapes matching the facet orientation measured by LEED. Equilibrium and kinetic mechanisms for the faceting are discussed.

We report unambiguous atomic scale evidence demonstrating that the atom density in the ``1\ifmmode\times\else\texttimes\fi{}1'' high temperature phase of Si(111) is \ensuremath{\sim}6% higher than the 7\ifmmode\times\else\texttimes\fi{}7. Such evidence is provided by scanning tunneling microscopy observation of excess adatom density and related island formation on surfaces prepared by a novel method which provides a low step density. The presence of the excess adatom density is also correlated to the observation of areas of metastable reconstructions, i.e., 9\ifmmode\times\else\texttimes\fi{}9, 2\ifmmode\times\else\texttimes\fi{}2, c2\ifmmode\times\else\texttimes\fi{}4, and \ensuremath{\surd}3 \ifmmode\times\else\texttimes\fi{} \ensuremath{\surd}3 , much larger than previously reported and for the first time, the existence of metastable 11\ifmmode\times\else\texttimes\fi{}11 on Si(111).

The structural stability of the Si(112) surface has been investigated using low-energy electron diffraction (LEED). Heating between 950° C and 1150° C causes faceting to the (111), (113), (525) and (255) surfaces unless great care is taken to prevent carbon contamination. Increasing the carbon concentration increases the extent of the faceting. Removal of the carbon by heating to 1250° C returns the surface orientation to (112).