Controlling DOPA adsorption via interacting with polyelectrolytes: layer structure and corrosion resistance.

Protein adsorption on polyelectrolyte (PE) surfaces has aroused intensive attraction, but there are still few investigations on tuning the protein adsorption at a solid surface by controllable layer structures and surface properties of PE adlayers. Furthermore, there is a lack of understanding regarding the correlation between molecular conformation and anticorrosion performance of composite materials. With this in mind, we synthesized a series of PEs and constructed 3,4-dihydroxy-l-phenylalanine (l-DOPA) adlayers on the PE surfaces, monitoring the whole adsorption process in situ. A highly charged cationic PE surface exhibits a low adhesion of DOPA molecules, leading to a loose structure, rough surface morphology, and strong solvation effects and, accordingly, this kind of multilayer provides a poor anticorrosion capacity. In comparison, amphiphilic and highly charged cationic PE surfaces are in favor of DOPA adsorption and the formation of compact and smooth multilayers due to cation-π and hydrophobic interactions between DOPA and PEs. Interestingly, one of the multilayers exhibits a remarkable enhancement of inhibition efficiency of about 460-fold compared with that of the bare substrate, which is much higher than that of other anticorrosion coatings reported previously. Our findings reveal the interaction mechanism between DOPA and PE surfaces to achieve the controllable adsorption of biomolecules, providing a promising way to optimize the layer structures to improve the anticorrosion capacity.

Direct Evidence of Salinity and pH Effects on the Interfacial Interactions of Asphaltene-Brine-Silica Systems.

The hydraulic fracturing technique remains essential to unlock fossil fuel from shale oil reservoirs. However, water imbibed by shale during hydraulic fracturing triggers environmental and technical challenges due to the low flowback water recovery. While it appears that the imbibition of fracturing fluid is a complex function of physico-chemical processes in particular capillary force which is associated with wettability of oil-brine-shale, the controlling factor(s) to govern the wettability is incomplete and the literature data in this context is missing. We thus measured the adsorption/desorption of asphaltenes on silica surface in the presence of brines using quartz crystal microbalance with dissipation (QCM-D). We detected zeta potential of asphaltene-brine and brine-silica systems and calculated the disjoining pressures of the asphaltene-brine-silica system in the case of different salinity. Moreover, we performed a geochemical study to quantify the variation of surface chemical species at asphaltene and silica surfaces with different pH values and used the chemical force microscope (CFM) method to quantify the effect of pH on intermolecular forces. Our results show that lowering salinity or raising pH reduced the adhesion force between asphaltene and silica surface. For example, at a pH value of 6.5, when the concentration of injected water is reduced from 1000 mM to 100 mM and 10 mM, the adhesion force decreased by approximately 58% and 66%, respectively. In addition, for the 100 mM NaCl solution, when the pH value increased from 4.5 to 6.5 and 9, the adhesion force decreased by approximately 56% and 87%, respectively. Decreased adhesion forces between asphaltene and the silica surface could promote the desorption of asphaltene from the silica surface, resulting in a negative zeta potential for both asphaltene-silica and brine-silica interfaces and a shift of wettability towards water-wet characteristic. During such a process, -NH+ number at asphaltene surfaces decreases and the bonds between -NH+ and >SiO- break down, to further interpret the formation of a thinner asphaltene adlayer on the rock surface. This study proposes a reliable theoretical basis for the application of hydraulic fracturing technology, and a facile and possible manipulation strategy to increase flowback water from unconventional reservoirs.

A pH and salt dually responsive emulsion in the presence of amphiphilic macromolecules.

A pH and salt dually responsive emulsion has been designed on the basis of a novel amphiphilic macromolecule. It was found that the water separation of an oil-in-water emulsion reached up to ∼60% after standing for 10 min at low pH. 2-(Diethylamino)ethyl methacrylate (DEA) residues were found to induce the macromolecules to protonate and to be hydrophilic at pH values between 2 and 6, resulting in dewetting from oil droplet surfaces in water. Besides, the macromolecules form aggregates with different structures at the water/oil interface, depending on the pH value or salt concentration of the emulsion system, enabling the system to be demulsified in response to the pH or salt stimulus. The experimental results also showed that with the addition of aluminium chloride at 100 mg L-1, the water separation was about 70% after 20 min. A possible mechanism with respect to demulsifying was proposed on the basis of an "ion bridge" among sodium acrylate (SA) residues, inducing the macromolecules to "cross-link" and become insoluble, and leading to oil/water separation. Furthermore, at a fixed pH of 5, addition of salt to the aqueous dispersion increased the degree of oil-water interfacial activity and batch emulsions were significantly unstable to coalesce at a low salinity of 25-50 mg L-1. This finding presents a new manipulation on emulsion stability and potential applications in the fields of oil recovery, wastewater treatment, sludge removal, and so on.

Self-Assembly Behavior of Ultrahighly Charged Amphiphilic Polyelectrolyte on Solid Surfaces.

The adsorption process of a geminized amphiphilic polyelectrolyte, comprising double elementary charges and double hydrophobic tails in each repeat unit (denoted as PAGC8), was investigated and characterized by means of quartz crystal microbalance with dissipation (QCM-D), ellipsometry, and atomic force microscopy (AFM). By comparison, the self-assembly behaviors of a traditional polyelectrolyte without hydrophobic chains (denoted as PASC1) and an amphiphilic polyelectrolyte with a single hydrophilic headgroup and hydrophobic tail in each repeat unit (denoted as PASC8) at the solid/liquid interface were also investigated in parallel. A two-regime buildup was found in both amphiphilic systems of PASC8 and PAGC8, where the first regime was dependent on electrostatic interactions between polyelectrolytes and oppositely charged substrates, and the rearrangements of the preadsorbed chains and their aggregation behaviors on surface dominated the second regime. Furthermore, it was found that the adsorbed amount and conformation changed as a function of the charge density and bulk concentrations of the polyelectrolytes. The comparison of the adsorbed mass obtained from QCM-D and ellipsometry allowed calculating the coupling water content which reached high values and indicated a flexible aggregate conformation in the presence of PAGC8, resulting in controlling the suspension stability even at an extremely low concentration. In order to provide an insight into the mechanism of the suspension stability of colloidal dispersions, we gave a further explanation with respect to the interactions between surfaces in the presence of the geminized polyelectrolyte.

Adjuvant Radiotherapy for Dissected Parotid Lymph Nodes Containing Merkel Cell Carcinoma from an Unknown Primary Skin Site: 2 Case Reports.

BACKGROUND Merkel cell carcinoma (MCC) is an aggressive neuroendocrine malignancy that has increased in incidence in recent decades. The management of MCC should involve multidisciplinary experts to achieve optimal patient outcomes. Radiotherapy is commonly used as adjuvant therapy. Our literature review of MCC indicates that aggressive adjuvant radiotherapy might have a positive impact on overall local control and survival. CASE REPORT The first case is a 75-year-old male patient who discovered a right preauricular mass 2 weeks prior. He underwent right parotidectomy with tumor removal on 2012/07/09, and pathology revealed MCC in 3 lymph nodes. The patient received postoperative adjuvant radiotherapy (61.2 Gy) to the remaining right parotid tumor bed and right neck lymph nodes. The patient refused adjuvant chemotherapy. During long-term follow-up, the patient remained disease free for 10 years. The other case is a 73-year-old female patient with metastatic MCC in a left parotid lymph node. She also underwent left parotidectomy with tumor removal, and pathological staging performed according to the 8th edition of the AJCC staging system showed pTxN1aMx, stage IIIA. After the operation, she received postoperative adjuvant radiotherapy (56 Gy) to the remaining left parotid and left neck lymph nodes. The patient remained disease free for 14 months. CONCLUSIONS Metastatic MCC of the parotid lymph nodes without a detectable primary skin tumor is very rare. Adjuvant radiotherapy to the tumor bed and regional nodal basin might be beneficial for preventing disease recurrence despite the absence of systemic medical therapy.

Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond.

The class of random features is one of the most popular techniques to speed up kernel methods in large-scale problems. Related works have been recognized by the NeurIPS Test-of-Time award in 2017 and the ICML Best Paper Finalist in 2019. The body of work on random features has grown rapidly, and hence it is desirable to have a comprehensive overview on this topic explaining the connections among various algorithms and theoretical results. In this survey, we systematically review the work on random features from the past ten years. First, the motivations, characteristics and contributions of representative random features based algorithms are summarized according to their sampling schemes, learning procedures, variance reduction properties and how they exploit training data. Second, we review theoretical results that center around the following key question: how many random features are needed to ensure a high approximation quality or no loss in the empirical/expected risks of the learned estimator. Third, we provide a comprehensive evaluation of popular random features based algorithms on several large-scale benchmark datasets and discuss their approximation quality and prediction performance for classification. Last, we discuss the relationship between random features and modern over-parameterized deep neural networks (DNNs), including the use of high dimensional random features in the analysis of DNNs as well as the gaps between current theoretical and empirical results. This survey may serve as a gentle introduction to this topic, and as a users' guide for practitioners interested in applying the representative algorithms and understanding theoretical results under various technical assumptions. We hope that this survey will facilitate discussion on the open problems in this topic, and more importantly, shed light on future research directions. Due to the page limit, we suggest the readers refer to the full version of this survey https://arxiv.org/abs/2004.11154.

Towards a Unified Quadrature Framework for Large-Scale Kernel Machines.

In this paper, we develop a quadrature framework for large-scale kernel machines via a numerical integration representation. Considering that the integration domain and measure of typical kernels, e.g., Gaussian kernels, arc-cosine kernels, are fully symmetric, we leverage a numerical integration technique, deterministic fully symmetric interpolatory rules, to efficiently compute quadrature nodes and associated weights for kernel approximation. Thanks to the full symmetric property, the applied interpolatory rules are able to reduce the number of needed nodes while retaining a high approximation accuracy. Further, we randomize the above deterministic rules by the classical Monte-Carlo sampling and control variates techniques with two merits: 1) The proposed stochastic rules make the dimension of the feature mapping flexibly varying, such that we can control the discrepancy between the original and approximate kernels by tuning the dimnension. 2) Our stochastic rules have nice statistical properties of unbiasedness and variance reduction. In addition, we elucidate the relationship between our deterministic/stochastic interpolatory rules and current typical quadrature based rules for kernel approximation, thereby unifying these methods under our framework. Experimental results on several benchmark datasets show that our methods compare favorably with other representative kernel approximation based methods.

Extended Kalman filters for nonlinear fractional-order systems perturbed by colored noises.

The fractional-order extended Kalman filter (FEKF) algorithm for a nonlinear fractional-order system perturbed by the colored noise is presented. Firstly, the first-order Taylor expansion is employed to linearize the nonlinear functions in the estimated system. Then, Grünwald-Letnikov difference (GLD) and the concept of fractional-order average derivative (FOAD) are employed to discretize nonlinear fractional-order systems perturbed by colored fractional-order process or measurement noise. An augmented system determined by the state and colored noises is presented to treat colored noises. Hence, the FEKFs using GLD and FOAD are carried out, respectively. By comparing two kinds of Kalman filters, FEKFs using FODA can gain the better effect of filtering for colored process or measurement noise to raise the estimation precision. Finally, we discuss three examples to show the validity of investigated FEKFs.

A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels.

Random Fourier features (RFFs) have been successfully employed to kernel approximation in large-scale situations. The rationale behind RFF relies on Bochner's theorem, but the condition is too strict and excludes many widely used kernels, e.g., dot-product kernels (violates the shift-invariant condition) and indefinite kernels [violates the positive definite (PD) condition]. In this article, we present a unified RFF framework for indefinite kernel approximation in the reproducing kernel Krein spaces (RKKSs). Besides, our model is also suited to approximate a dot-product kernel on the unit sphere, as it can be transformed into a shift-invariant but indefinite kernel. By the Kolmogorov decomposition scheme, an indefinite kernel in RKKS can be decomposed into the difference of two unknown PD kernels. The spectral distribution of each underlying PD kernel can be formulated as a nonparametric Bayesian Gaussian mixtures model. Based on this, we propose a double-infinite Gaussian mixture model in RFF by placing the Dirichlet process prior. It takes full advantage of high flexibility on the number of components and has the capability of approximating indefinite kernels on a wide scale. In model inference, we develop a non-conjugate variational algorithm with a sub-sampling scheme for the posterior inference. It allows for the non-conjugate case in our model and is quite efficient due to the sub-sampling strategy. Experimental results on several large classification data sets demonstrate the effectiveness of our nonparametric Bayesian model for indefinite kernel approximation when compared to other representative random feature-based methods.

Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure.

In kernel methods, the kernels are often required to be positive definitethat restricts the use of many indefinite kernels. To consider those nonpositive definite kernels, in this paper, we aim to build an indefinite kernel learning framework for kernel logistic regression (KLR). The proposed indefinite KLR (IKLR) model is analyzed in the reproducing kernel Krein spaces and then becomes nonconvex. Using the positive decomposition of a nonpositive definite kernel, the derived IKLR model can be decomposed into the difference of two convex functions. Accordingly, a concave-convex procedure (CCCP) is introduced to solve the nonconvex optimization problem. Since the CCCP has to solve a subproblem in each iteration, we propose a concave-inexact-convex procedure (CCICP) algorithm with an inexact solving scheme to accelerate the solving process. Besides, we propose a stochastic variant of CCICP to efficiently obtain a proximal solution, which achieves the similar purpose with the inexact solving scheme in CCICP. The convergence analyses of the above-mentioned two variants of CCCP are conducted. By doing so, our method works effectively not only in a deterministic setting but also in a stochastic setting. Experimental results on several benchmarks suggest that the proposed IKLR model performs favorably against the standard (positive definite) KLR and other competitive indefinite learning-based algorithms.

Coexistence of Antiadhesion Performance, Intrinsic Stretchability, and Transparency.

Antiadhesion performance, stretchability, and transparency are highly desirable properties for materials and devices in numerous applications. However, the existing strategies for imparting materials with antiadhesion performance generally induce rigidity and opacity, and principle is yet to be provided for designing materials that combine these important parameters. Here, we show that four factors including a low surface energy, appropriate cross-linking, availability of a homogeneous and amorphous composite, and a smooth material surface can be used to design an intrinsically stretchable and transparent polymer film with antiadhesion performance against various liquids including water, diiodomethane, hexadecane, cooking oil, and pump oil. The film can be obtained via simply molding a waterborne polymer network at ambient temperature. Furthermore, the film can retain its antiadhesion performance and outstanding transparency even when it is subjected to large mechanical deformations reaching up to 1800%, and its maximal fracture strain exceeds 3000%. These design concepts offer a general platform for achieving multiple material functionalities, and may open new avenues for the surface functionalization of stretchable materials and devices.

Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning.

In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank. The proposed dictionary learning technique incorporates a reconstruction error that improves the reliability of classification. Also, a multiconstraint objective function is designed to enable active learning of a discriminative and robust dictionary. Further, an optimal solution is obtained by iteratively computing the dictionary, coefficients, and by simultaneously learning the classifier parameters. Finally, a simple yet effective likelihood function is implemented to estimate the optimal state of the target during tracking. Moreover, to make the dictionary adaptive to the variations of the target and background during tracking, an online update criterion is employed while learning the new dictionary. Experimental results on a publicly available benchmark dataset have demonstrated that the proposed tracking algorithm performs better than other state-of-the-art trackers.

Robust Visual Tracking Revisited: From Correlation Filter to Template Matching.

In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the correlation operation in CFTs, a sophisticated similarity metric termed mutual buddies similarity is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named memory, which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively understand the target appearance variations, recall some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers.

PAMAM-Based Dendrimers with Different Alkyl Chains Self-Assemble on Silica Surfaces: Controllable Layer Structure and Molecular Aggregation.

Amphiphilic poly(amidoamine) (PAMAM) dendrimers are a well-known dendritic family due to their remarkable ability to self-assemble on solid surface. However, the relationship between molecular conformation (or adsorption kinetics) of a self-assembled layer and molecular amphiphilicity of such kind of dendrimer is still lacking, which limits the development of modulating self-assembling structures and surface functionality. With this in mind, we synthesized a series of amphiphilic PAMAM-based dendrimers, denoted as G1C n, with different alkyl chains ( n = 8, 12, and 16), and investigated the molecular aggregation on silica surfaces by means of quartz crystal microbalance with dissipation, atomic force microscopy, and contact angle. After rinsing, remaining adsorption amounts of G1C12 were higher than those of G1C8 at high concentrations, suggesting that G1C12 adlayers were more stable due to the stronger intermolecular hydrophobic interactions, whereas it preferred to adopt the intramolecular hydrophobic interactions for G1C16, with low adsorption amounts and unstable adlayers. Bilayer-like structures were inferred in G1C8 and G1C12 adlayers with loose conformation, whereas monolayer structures were likely to exist in the sparse adsorption film of G1C16. Our results provided more detailed understanding of the effect of molecular structure on the self-assembled structures of amphiphilic dendrimers on solid surfaces, shedding light on the controlled microstructure and wettability of functional surface by modulating the length of hydrophobic chains of dendrimers and a potential application of dendrimer-substrate combinations.

Thermodynamic Investigation of the Effect of Interface Curvature on the Solid-Liquid Equilibrium and Eutectic Point of Binary Mixtures.

Thermodynamic phase behavior is affected by curved interfaces in micro- and nanoscale systems. For example, capillary freezing point depression is associated with the pressure difference between the solid and liquid phases caused by interface curvature. In this study, the thermal, mechanical, and chemical equilibrium conditions are derived for binary solid-liquid equilibrium with a curved solid-liquid interface due to confinement in a capillary. This derivation shows the equivalence of the most general forms of the Gibbs-Thomson and Ostwald-Freundlich equations. As an example, the effect of curvature on solid-liquid equilibrium is explained quantitatively for the water/glycerol system. Considering the effect of a curved solid-liquid interface, a complete solid-liquid phase diagram is developed over a range of concentrations for the water/glycerol system (including the freezing of pure water or precipitation of pure glycerol depending on the concentration of the solution). This phase diagram is compared with the traditional phase diagram in which the assumption of a flat solid-liquid interface is made. We show the extent to which nanoscale interface curvature can affect the composition-dependent freezing and precipitating processes, as well as the change in the eutectic point temperature and concentration with interface curvature. Understanding the effect of curvature on solid-liquid equilibrium in nanoscale capillaries has applications in the food industry, soil science, cryobiology, nanoporous materials, and various nanoscience fields.

Mapping of a major stripe rust resistance gene in Chinese native wheat variety Chike using microsatellite markers.

Chike (accession number Su1900), a Chinese native wheat (Triticum aestivum L.) variety, is resistant to the currently prevailing physiological races of Puccinia striiformis Westend. f. sp. tritici in China. Genetic analysis indicated that resistance to the physiological race CY32 of the pathogen in the variety was controlled by one dominant gene. In this study, BSA (bulked segregant analysis) methods and SSRs (simple sequence repeats) marker polymorphic analysis are used to map the gene. The resistant and susceptible DNA bulks were prepared from the segregating F2 population of the cross between Taichung 29, a susceptible variety as maternal parent, and Chike as paternal parent. Over 400 SSR primers were screened, and five SSR markers Xwmc44, Xgwm259, Xwmc367, Xcfa2292, and Xbarc80 on the chromosome arm 1BL were found to be polymorphic between the resistant and the susceptible DNA bulks as well as their parents. Genetic linkage was tested on segregating F2 population with 200 plants, including 140 resistant and 60 susceptible plants. All the five SSR markers were linked to the stripe rust resistance gene in Chike. The genetic distances for the markers Xwmc44, Xgwm259, Xwmc367, Xcfa2292, and Xbarc80 to the target gene were 8.3 cM, 9.1 cM, 17.2 cM, 20.6 cM, and 31.6 cM, respectively. Analysis using 21 nulli-tetrasomic Chinese Spring lines further confirmed that all the five markers were located on chromosome 1B. On the basis of the above results, it is reasonable to assume that the major stripe rust resistance gene YrChk in Chike was located on the chromosome arm 1BL, and its comparison with the other stripe rust resistance genes located on 1B suggested that YrChk may be a novel gene that provides the resistance against stripe rust in Chike. Exploration and utilization of resources of disease resistance genes in native wheat varieties will be helpful both to diversify the resistance genes and to amend the situation of resistance gene simplification in the commercial wheat cultivars in China.