Flexible skin-attachable strain-gauge sensors are an essential component in the development of artificial systems that can mimic the complex characteristics of the human skin. In general, such sensors contain a number of circuits or complex layered matrix arrays. Here, we present a simple architecture for a flexible and highly sensitive strain sensor that enables the detection of pressure, shear and torsion. The device is based on two interlocked arrays of high-aspect-ratio Pt-coated polymeric nanofibres that are supported on thin polydimethylsiloxane layers. When different sensing stimuli are applied, the degree of interconnection and the electrical resistance of the sensor changes in a reversible, directional manner with specific, discernible strain-gauge factors. The sensor response is highly repeatable and reproducible up to 10,000 cycles with excellent on/off switching behaviour. We show that the sensor can be used to monitor signals ranging from human heartbeats to the impact of a bouncing water droplet on a superhydrophobic surface.
A new sample preparation procedure, termed pH-controlled dispersive liquid-liquid microextraction (pH-DLLME), has been developed for the analysis of ionisable compounds in highly complex matrices. This DLLME mode, intended to improve the selectivity and to expand the application range of DLLME, is based on two successive DLLMEs conducted at opposite pH values. pH-DLLME was applied to determination of ochratoxin A (OTA) in cereals. The hydrophobic matrix interferences in the raw methanol extract (disperser, 1mL) were removed by a first DLLME (I DLLME) performed at pH 8 to reduce the solubility of OTA in the extractant (CCl(4), 400μL). The pH of the aqueous phase was then adjusted to 2, and the analyte was extracted and concentrated by a second DLLME (extractant, 150μL C(2)H(4)Br(2)). The main factors influencing the efficiency of pH-DLLME including type and volume of I DLLME extractant, as well as the parameters affecting the OTA extraction by II DLLME, were studied in detail. Under optimum conditions, the method has detection and quantification limits of 0.019 and 0.062μgkg(-1), respectively, with OTA recoveries in the range of 81.2-90.1% (n=3). The accuracy of the analytical procedure, evaluated with a reference material (cereal naturally contaminated with OTA), is acceptable (accuracy of 85.6%±1.7, n=5). The applicability of pH-DLLME to the selective extraction of other ionisable compounds, such as acidic and basic pharmaceutical products was also demonstrated. The additional advantages of pH-DLLME are a higher selectivity and the extension of this microextraction technique to highly complex matrices.
- Proceedings of the National Academy of Sciences of the United States of America
- Published almost 4 years ago
In directed graphs, relationships are asymmetric and these asymmetries contain essential structural information about the graph. Directed relationships lead to a new type of clustering that is not feasible in undirected graphs. We propose a spectral co-clustering algorithm called di-sim for asymmetry discovery and directional clustering. A Stochastic co-Blockmodel is introduced to show favorable properties of di-sim To account for the sparse and highly heterogeneous nature of directed networks, di-sim uses the regularized graph Laplacian and projects the rows of the eigenvector matrix onto the sphere. A nodewise asymmetry score and di-sim are used to analyze the clustering asymmetries in the networks of Enron emails, political blogs, and the Caenorhabditis elegans chemical connectome. In each example, a subset of nodes have clustering asymmetries; these nodes send edges to one cluster, but receive edges from another cluster. Such nodes yield insightful information (e.g., communication bottlenecks) about directed networks, but are missed if the analysis ignores edge direction.
- IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
- Published almost 4 years ago
We address the problem of face video retrieval in TV-series which searches video clips based on the presence of specific character, given one face track of his/her. This is tremendously challenging because on one hand, faces in TV-series are captured in largely uncontrolled conditions with complex appearance variations, and on the other hand retrieval task typically needs efficient representation with low time and space complexity. To handle this problem, we propose a compact and discriminative representation for the huge body of video data, named Compact Video Code (CVC). Our method first models the face track by its sample (i.e., frame) covariance matrix to capture the video data variations in a statistical manner. To incorporate discriminative information and obtain more compact video signature suitable for retrieval, the high-dimensional covariance representation is further encoded as a much lower-dimensional binary vector, which finally yields the proposed CVC. Specifically, each bit of the code, i.e., each dimension of the binary vector, is produced via supervised learning in a max margin framework, which aims to make a balance between the discriminability and stability of the code. Besides, we further extend the descriptive granularity of covariance matrix from traditional pixel-level to more general patchlevel, and proceed to propose a novel hierarchical video representation named Spatial Pyramid Covariance (SPC) along with a fast calculation method. Face retrieval experiments on two challenging TV-series video databases, i.e., the Big Bang Theory and Prison Break, demonstrate the competitiveness of the proposed CVC over state-of-the-art retrieval methods. In addition, as a general video matching algorithm, CVC is also evaluated in traditional video face recognition task on a standard Internet database, i.e., YouTube Celebrities, showing its quite promising performance by using an extremely compact code with only 128 bits.
- IEEE transactions on visualization and computer graphics
- Published almost 3 years ago
Bundling visually aggregates curves to reduce clutter and help finding important patterns in trail-sets or graph drawings. We propose a new approach to bundling based on functional decomposition of the underling dataset. We recover the functional nature of the curves by representing them as linear combinations of piecewise-polynomial basis functions with associated expansion coefficients. Next, we express all curves in a given cluster in terms of a centroid curve and a complementary term, via a set of so-called principal component functions. Based on the above, we propose a two-fold contribution: First, we use cluster centroids to design a new bundling method for 2D and 3D curve-sets. Secondly, we deform the cluster centroids and generate new curves along them, which enables us to modify the underlying data in a statistically-controlled way via its simplified (bundled) view. We demonstrate our method by applications on real-world 2D and 3D datasets for graph bundling, trajectory analysis, and vector field and tensor field visualization.
Intravital imaging of BRAF-mutant melanoma cells containing an ERK/MAPK biosensor reveals how the tumor microenvironment affects response to BRAF inhibition by PLX4720. Initially, melanoma cells respond to PLX4720, but rapid reactivation of ERK/MAPK is observed in areas of high stromal density. This is linked to "paradoxical" activation of melanoma-associated fibroblasts by PLX4720 and the promotion of matrix production and remodeling leading to elevated integrin β1/FAK/Src signaling in melanoma cells. Fibronectin-rich matrices with 3-12 kPa elastic modulus are sufficient to provide PLX4720 tolerance. Co-inhibition of BRAF and FAK abolished ERK reactivation and led to more effective control of BRAF-mutant melanoma. We propose that paradoxically activated MAFs provide a “safe haven” for melanoma cells to tolerate BRAF inhibition.
The number of large-scale high-dimensional datasets recording different aspects of a single disease is growing, accompanied by a need for frameworks that can create one coherent model from multiple tensors of matched columns, e.g., patients and platforms, but independent rows, e.g., probes. We define and prove the mathematical properties of a novel tensor generalized singular value decomposition (GSVD), which can simultaneously find the similarities and dissimilarities, i.e., patterns of varying relative significance, between any two such tensors. We demonstrate the tensor GSVD in comparative modeling of patient- and platform-matched but probe-independent ovarian serous cystadenocarcinoma (OV) tumor, mostly high-grade, and normal DNA copy-number profiles, across each chromosome arm, and combination of two arms, separately. The modeling uncovers previously unrecognized patterns of tumor-exclusive platform-consistent co-occurring copy-number alterations (CNAs). We find, first, and validate that each of the patterns across only 7p and Xq, and the combination of 6p+12p, is correlated with a patient’s prognosis, is independent of the tumor’s stage, the best predictor of OV survival to date, and together with stage makes a better predictor than stage alone. Second, these patterns include most known OV-associated CNAs that map to these chromosome arms, as well as several previously unreported, yet frequent focal CNAs. Third, differential mRNA, microRNA, and protein expression consistently map to the DNA CNAs. A coherent picture emerges for each pattern, suggesting roles for the CNAs in OV pathogenesis and personalized therapy. In 6p+12p, deletion of the p21-encoding CDKN1A and p38-encoding MAPK14 and amplification of RAD51AP1 and KRAS encode for human cell transformation, and are correlated with a cell’s immortality, and a patient’s shorter survival time. In 7p, RPA3 deletion and POLD2 amplification are correlated with DNA stability, and a longer survival. In Xq, PABPC5 deletion and BCAP31 amplification are correlated with a cellular immune response, and a longer survival.
Heart regeneration through in vivo cardiac reprogramming has been demonstrated as a possible regenerative strategy. While it has been reported that cardiac reprogramming in vivo is more efficient than in vitro, the influence of the extracellular microenvironment on cardiac reprogramming remains incompletely understood. This understanding is necessary to improve the efficiency of cardiac reprogramming in order to implement this strategy successfully. Here we have identified matrix identity and cell-generated tractional forces as key determinants of the dedifferentiation and differentiation stages during reprogramming. Cell proliferation, matrix mechanics, and matrix microstructure are also important, but play lesser roles. Our results suggest that the extracellular microenvironment can be optimized to enhance cardiac reprogramming.
In many biological systems, the network of interactions between the elements can only be inferred from experimental measurements. In neuroscience, non-invasive imaging tools are extensively used to derive either structural or functional brain networks in-vivo. As a result of the inference process, we obtain a matrix of values corresponding to a fully connected and weighted network. To turn this into a useful sparse network, thresholding is typically adopted to cancel a percentage of the weakest connections. The structural properties of the resulting network depend on how much of the inferred connectivity is eventually retained. However, how to objectively fix this threshold is still an open issue. We introduce a criterion, the efficiency cost optimization (ECO), to select a threshold based on the optimization of the trade-off between the efficiency of a network and its wiring cost. We prove analytically and we confirm through numerical simulations that the connection density maximizing this trade-off emphasizes the intrinsic properties of a given network, while preserving its sparsity. Moreover, this density threshold can be determined a-priori, since the number of connections to filter only depends on the network size according to a power-law. We validate this result on several brain networks, from micro- to macro-scales, obtained with different imaging modalities. Finally, we test the potential of ECO in discriminating brain states with respect to alternative filtering methods. ECO advances our ability to analyze and compare biological networks, inferred from experimental data, in a fast and principled way.
Prediction of adult performance from early age talent identification in sport remains difficult. Talent identification research has generally been performed using univariate analysis, which ignores multivariate relationships. To address this issue, this study used a novel higher-dimensional model to orthogonalize multivariate anthropometric and fitness data from junior rugby league players, with the aim of differentiating future career attainment. Anthropometric and fitness data from 257 Under-15 rugby league players was collected. Players were grouped retrospectively according to their future career attainment (i.e., amateur, academy, professional). Players were blindly and randomly divided into an exploratory (n = 165) and validation dataset (n = 92). The exploratory dataset was used to develop and optimize a novel higher-dimensional model, which combined singular value decomposition (SVD) with receiver operating characteristic analysis. Once optimized, the model was tested using the validation dataset. SVD analysis revealed 60 m sprint and agility 505 performance were the most influential characteristics in distinguishing future professional players from amateur and academy players. The exploratory dataset model was able to distinguish between future amateur and professional players with a high degree of accuracy (sensitivity = 85.7%, specificity = 71.1%; p<0.001), although it could not distinguish between future professional and academy players. The validation dataset model was able to distinguish future professionals from the rest with reasonable accuracy (sensitivity = 83.3%, specificity = 63.8%; p = 0.003). Through the use of SVD analysis it was possible to objectively identify criteria to distinguish future career attainment with a sensitivity over 80% using anthropometric and fitness data alone. As such, this suggests that SVD analysis may be a useful analysis tool for research and practice within talent identification.