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  1. Free, publicly-accessible full text available May 1, 2023
  2. Co-evolving sequences are ubiquitous in a variety of applications, where different sequences are often inherently inter-connected with each other. We refer to such sequences, together with their inherent connections modeled as a structured network, as network of co-evolving sequences (NoCES). Typical NoCES applications in- clude road traffic monitoring, company revenue prediction, motion capture, etc. To date, it remains a daunting challenge to accurately model NoCES due to the coupling between network structure and sequences. In this paper, we propose to modeling NoCES with the aim of simultaneously capturing both the dynamics and the inter- play between network structure and sequences.more »Specifically, we propose a joint learning framework to alternatively update the network representations and sequence representations as the se- quences evolve over time. A unique feature of our framework lies in that it can deal with the case when there are co-evolving sequences on both network nodes and edges. Experimental evaluations on four real datasets demonstrate that the proposed approach (1) out- performs the existing competitors in terms of prediction accuracy, and (2) scales linearly w.r.t. the sequence length and the network size.« less
    Free, publicly-accessible full text available April 1, 2023
  3. Hybrid microfluidic systems that are composed of multiple different types of substrates have been recognized as a versatile and superior platform, which can draw benefits from different substrates while avoiding their limitations. This review article introduces the recent innovations of different types of low-cost hybrid microfluidic devices, particularly focusing on cost-effective polymer- and paper-based hybrid microfluidic devices. In this article, the fabrication of these hybrid microfluidic devices is briefly described and summarized. We then highlight various hybrid microfluidic systems, including polydimethylsiloxane (PDMS)-based, thermoplastic-based, paper/polymer hybrid systems, as well as other emerging hybrid systems (such as thread-based). The special benefits ofmore »using these hybrid systems have been summarized accordingly. A broad range of biological and biomedical applications using these hybrid microfluidic devices are discussed in detail, including nucleic acid analysis, protein analysis, cellular analysis, 3D cell culture, organ-on-a-chip, and tissue engineering. The perspective trends of hybrid microfluidic systems involving the improvement of fabrication techniques and broader applications are also discussed at the end of the review.« less
    Free, publicly-accessible full text available July 13, 2022
  4. Attributed network embedding aims to learn low dimensional node representations by combining both the network's topological structure and node attributes. Most of the existing methods either propagate the attributes over the network structure or learn the node representations by an encoder-decoder framework. However, propagation based methods tend to prefer network structure to node attributes, whereas encoder-decoder methods tend to ignore the longer connections beyond the immediate neighbors. In order to address these limitations while enjoying the best of the two worlds, we design cross fusion layers for unsupervised attributed network embedding. Specifically, we first construct two separate views to handlemore »network structure and node attributes, and then design cross fusion layers to allow flexible information exchange and integration between the two views. The key design goals of the cross fusion layers are three-fold: 1) allowing critical information to be propagated along the network structure, 2) encoding the heterogeneity in the local neighborhood of each node during propagation, and 3) incorporating an additional node attribute channel so that the attribute information will not be overshadowed by the structure view. Extensive experiments on three datasets and three downstream tasks demonstrate the effectiveness of the proposed method.« less
  5. Network embedding aims to automatically learn the node representations in networks. The basic idea of network embedding is to first construct a network to describe the neighborhood context for each node, and then learn the node representations by designing an objective function to preserve certain properties of the constructed context network. The vast majority of the existing methods, explicitly or implicitly, follow a pointwise design principle. That is, the objective can be decomposed into the summation of the certain goodness function over each individual edge of the context network. In this paper, we propose to go beyond such pointwise approaches,more »and introduce the ranking-oriented design principle for network embedding. The key idea is to decompose the overall objective function into the summation of a goodness function over a set of edges to collectively preserve their relative rankings on the context network. We instantiate the ranking-oriented design principle by two new network embedding algorithms, including a pairwise network embedding method PaWine which optimizes the relative weights of edge pairs, and a listwise method LiWine which optimizes the relative weights of edge lists. Both proposed algorithms bear a linear time complexity, making themselves scalable to large networks. We conduct extensive experimental evaluations on five real datasets with a variety of downstream learning tasks, which demonstrate that the proposed approaches consistently outperform the existing methods.« less
  6. Continuous and accurate decoding of intended motions is critical for human-machine interactions. Here, we developed a novel approach for real-time continuous prediction of forces in individual fingers using parallel convolutional neural networks (CNNs). We extracted populational motor unit discharge frequency using CNNs organized in a parallel structure. The CNNs parameters were trained based on two features from high-density electromyogram (HD-EMG), namely temporal energy heatmaps and frequency spectrum maps. The populational motor unit discharge frequency was then used to continuously predict finger forces based on a linear regression model. The force prediction performance was compared with a motor unit decomposition methodmore »and the conventional EMG amplitude-based method. Our results showed that the correlation coefficient between the predicted and the recorded forces of the parallel CNN approach was on average 0.91, compared with an offline decomposition method of 0.89, an online decomposition method of 0.82, and the EMG amplitude method of 0.81. Additionally, the CNN based approach showed generalizable performance, with CNN trained on one finger applying to a different finger. The outcomes suggest that our CNN based algorithm can offer an accurate and efficient force decoding method for human-machine interactions.« less
  7. Photoactivatable protecting groups (PPGs) are useful for a broad range of applications ranging from biology to materials science. In chemical biology, induction of biological processes via photoactivation is a powerful strategy for achieving spatiotemporal control. The importance of cysteine, glutathione, and other bioactive thiols in regulating protein structure/activity and cell redox homeostasis makes modulation of thiol activity particularly useful. One major objective for enhancing the utility of photoactivatable protecting groups (PPGs) in living systems is creating PPGs with longer wavelength absorption maxima and efficient two-photon (TP) absorption. Toward these objectives, we developed a carboxyl- and dimethylamine-functionalized nitrodibenzofuran PPG scaffold (cDMA-NDBF)more »for thiol photoactivation, which has a bathochromic shift in the one-photon absorption maximum from λ max = 315 nm with the unfunctionalized NDBF scaffold to λ max = 445 nm. While cDMA-NDBF-protected thiols are stable in the presence of UV irradiation, they undergo efficient broad-spectrum TP photolysis at wavelengths as long as 900 nm. To demonstrate the wavelength orthogonality of cDMA-NDBF and NDBF photolysis in a biological setting, caged farnesyltransferase enzyme inhibitors (FTI) were prepared and selectively photoactivated in live cells using 850–900 nm TP light for cDMA-NDBF-FTI and 300 nm UV light for NDBF-FTI. These experiments represent the first demonstration of thiol photoactivation at wavelengths above 800 nm. Consequently, cDMA-NDBF-caged thiols should have broad applicability in a wide range of experiments in chemical biology and materials science.« less
  8. A low-cost, sensitive, and disposable paper-based immunosensor for instrument-free colorimetric detection of pancreatic cancer biomarker PEAK1 was reported for the first time by capitalizing the catalytic properties of gold nanoparticles in colour dye degradation. This simple signal amplification method enhances the detection sensitivity by about 10 fold.