Real-time, depth-resolved, in vivo multiphoton fluorescence lifetime imaging microscopy of agricultural herbicide treatments in plants.

The development of effective and safe agricultural treatments requires sub-cellular insight of the biochemical effects of treatments in living tissue in real-time. Industry-standard mass spectroscopic imaging lacks real-time in vivo capability. As an alternative, multiphoton fluorescence lifetime imaging microscopy (MPM-FLIM) allows for 3D sub-cellular quantitative metabolic imaging but is often limited to low frame rates.

The Spatiotemporal Dynamics of Insect Predator-Prey System Incorporating Refuge Effect.

The insect predator-prey system mediates several feedback mechanisms which regulate species abundance and spatial distribution. However, the spatiotemporal dynamics of such discrete systems with the refuge effect remain elusive. In this study, we analyzed a discrete Holling type II model incorporating the refuge effect using theoretical calculations and numerical simulations, and selected moths with high and low growth rates as two exemplifications.

Improving fluorescence lifetime imaging microscopy phasor accuracy using convolutional neural networks.

Although a powerful biological imaging technique, fluorescence lifetime imaging microscopy (FLIM) faces challenges such as a slow acquisition rate, a low signal-to-noise ratio (SNR), and high cost and complexity. To address the fundamental problem of low SNR in FLIM images, we demonstrate how to use pre-trained convolutional neural networks (CNNs) to reduce noise in FLIM measurements. Our approach uses pre-learned models that have been previously validated on large datasets with different distributions than the training datasets, such as sample structures, noise distributions, and microscopy modalities in fluorescence microscopy, to eliminate the need to train a neural network from scratch or to acquire a large training dataset to denoise FLIM data.

Rapid fabrication of the Au hexagonal cone arrays for SERS applications.

This study performed trace detection using surface-enhanced Raman scattering (SERS) on Au hexagonal cone arrays (Au-HCAs). Uniform porous anodized aluminum oxide (AAO) templates were used, and an Ag film with a cone cavity was prepared using a thermal deposition technique. Next, a series of homogeneous Au-HCAs were prepared controllably via electrodeposition growth technology.

A Hybrid Stochastic-Deterministic Minibatch Proximal Gradient Method for Efficient Optimization and Generalization.

Despite the success of stochastic variance-reduced gradient (SVRG) algorithms in solving large-scale problems, their stochastic gradient complexity often scales linearly with data size and is expensive for huge data. Accordingly, we propose a hybrid stochastic-deterministic minibatch proximal gradient~(HSDMPG) algorithm for strongly convex problems with linear prediction structure, e.g.

Bilayer and three dimensional conductive network composed by SnCl reduced rGO with CNTs and GO applied in transparent conductive films.

Graphene oxide (GO), reduced graphene oxide (rGO) and carbon nanotubes (CNTs) have their own advantages in electrical, optical, thermal and mechanical properties. An effective combination of these materials is ideal for preparing transparent conductive films to replace the traditional indium tin oxide films. At present, the preparation conditions of rGO are usually harsh and some of them have toxic effects.

Green tea polyphenolic antioxidants oxidize hydrogen sulfide to thiosulfate and polysulfides: A possible new mechanism underpinning their biological action.

Matcha and green tea catechins such as (-)-epicatechin (EC), (-)-epigallocatechin (EGC) and (-)-epigallocatechin gallate (EGCG) have long been studied for their antioxidant and health-promoting effects. Using specific fluorophores for HS (AzMC) and polysulfides (SSP4) as well as IC-MS and UPLC-MS/MS-based techniques we here show that popular Japanese and Chinese green teas and select catechins all catalytically oxidize hydrogen sulfide (HS) to polysulfides with the potency of EGC > EGCG >> EG. This reaction is accompanied by the formation of sulfite, thiosulfate and sulfate, consumes oxygen and is partially inhibited by the superoxide scavenger, tempol, and superoxide dismutase but not mannitol, trolox, DMPO, or the iron chelator, desferrioxamine.

Manganese Porphyrin-Based SOD Mimetics Produce Polysulfides from Hydrogen Sulfide.

Manganese-centered porphyrins (MnPs), MnTE-2-PyP (MnTE), MnTnHex-2-PyP (MnTnHex), and MnTnBuOE-2-PyP (MnTnBuOE) have received considerable attention because of their ability to serve as superoxide dismutase (SOD) mimetics thereby producing hydrogen peroxide (HO), and oxidants of ascorbate and simple aminothiols or protein thiols. MnTE-2-PyP and MnTnBuOE-2-PyP are now in five Phase II clinical trials warranting further exploration of their rich redox-based biology. Previously, we reported that SOD is also a sulfide oxidase catalyzing the oxidation of hydrogen sulfide (HS) to hydrogen persulfide (HS) and longer-chain polysulfides (HS, = 3-7).

Improvement of Corrosion Resistance of Waterborne Polyurethane Coatings by Covalent and Noncovalent Grafted Graphene Oxide Nanosheets.

The amphiphilic graphene derivative was prepared by covalent grafting of graphene oxide (GO) with isophorone diisocyanate and ,-dimethylethanolamine and then noncovalent grafting of GO with sodium dodecylbenzenesulfonate. The results obtained from infrared spectroscopy, X-ray photoelectron spectroscopy, thermal gravimetric analysis, and X-ray diffraction analysis revealed that the short chains were successfully grafted onto the surface of GO. Subsequently, scanning electron microscopy and optical microscopy results showed that the modified GO (IP-GO) has the best dispersibility and compatibility than GO and reduced GO in the waterborne polyurethane matrix.

Multifunctional PVDF/CNT/GO mixed matrix membranes for ultrafiltration and fouling detection.

Membrane fouling can be effectively addressed by modifying the membrane to realize anti-fouling capability together with real-time fouling detection. Here, we present the synthesis and water treatment testing of a promising candidate for this application, a composite membrane of polyvinylidene fluoride (PVDF) and functionalized carbon nano-materials prepared by a facile phase inversion method. The synergistic effect of oxidized multi-walled carbon nanotubes (OMWCNTs) and graphene oxide (GO) enabled better surface pore structures, higher surface roughness, hydrophilicity, and better antifouling property as compared with that of pristine PVDF membranes.

Matrix Completion with Deterministic Sampling: Theories and Methods.

In some significant applications such as data forecasting, the locations of missing entries cannot obey any non-degenerate distributions, questioning the validity of the prevalent assumption that the missing data is randomly chosen according to some probabilistic model. To break through the limits of random sampling, we explore in this paper the problem of real-valued matrix completion under the setup of deterministic sampling. We propose two conditions, isomeric condition and relative well-conditionedness, for guaranteeing an arbitrary matrix to be recoverable from a sampling of the matrix entries.

Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian Manifolds.

First-order non-convex Riemannian optimization algorithms have gained recent popularity in structured machine learning problems including principal component analysis and low-rank matrix completion. The current paper presents an efficient Riemannian Stochastic Path Integrated Differential EstimatoR (R-SPIDER) algorithm to solve the finite-sum and online Riemannian non-convex minimization problems. At the core of R-SPIDER is a recursive semi-stochastic gradient estimator that can accurately estimate Riemannian gradient under not only exponential mapping and parallel transport, but also general retraction and vector transport operations.

Reversed Spectral Hashing.

Hashing is emerging as a powerful tool for building highly efficient indices in large-scale search systems. In this paper, we study spectral hashing (SH), which is a classical method of unsupervised hashing. In general, SH solves for the hash codes by minimizing an objective function that tries to preserve the similarity structure of the data given.

Newton-Type Greedy Selection Methods for $\ell _0$ -Constrained Minimization.

We introduce a family of Newton-type greedy selection methods for -constrained minimization problems. The basic idea is to construct a quadratic function to approximate the original objective function around the current iterate and solve the constructed quadratic program over the cardinality constraint. The next iterate is then estimated via a line search operation between the current iterate and the solution of the sparse quadratic program.

Efficient $\chi ^{2}$ Kernel Linearization via Random Feature Maps.

Explicit feature mapping is an appealing way to linearize additive kernels, such as χ kernel for training large-scale support vector machines (SVMs). Although accurate in approximation, feature mapping could pose computational challenges in high-dimensional settings as it expands the original features to a higher dimensional space. To handle this issue in the context of χ kernel SVMs learning, we introduce a simple yet efficient method to approximately linearize χ kernel through random feature maps.

Sparseness Analysis in the Pretraining of Deep Neural Networks.

A major progress in deep multilayer neural networks (DNNs) is the invention of various unsupervised pretraining methods to initialize network parameters which lead to good prediction accuracy. This paper presents the sparseness analysis on the hidden unit in the pretraining process. In particular, we use the L -norm to measure sparseness and provide some sufficient conditions for that pretraining leads to sparseness with respect to the popular pretraining models-such as denoising autoencoders (DAEs) and restricted Boltzmann machines (RBMs).

Autogrouped sparse representation for visual analysis.

In image classification, recognition or retrieval systems, image contents are commonly described by global features. However, the global features generally contain noise from the background, occlusion, or irrelevant objects in the images. Thus, only part of the global feature elements is informative for describing the objects of interest and useful for the image analysis tasks.

Forward basis selection for pursuing sparse representations over a dictionary.

The forward greedy selection algorithm of Frank and Wolfe has recently been applied with success to coordinate-wise sparse learning problems, characterized by a tradeoff between sparsity and accuracy. In this paper, we generalize this method to the setup of pursuing sparse representations over a prefixed dictionary. Our proposed algorithm iteratively selects an atom from the dictionary and minimizes the objective function over the linear combinations of all the selected atoms.

A joint matrix completion and filtering model for influenza serological data integration.

Antigenic characterization based on serological data, such as Hemagglutination Inhibition (HI) assay, is one of the routine procedures for influenza vaccine strain selection. In many cases, it would be impossible to measure all pairwise antigenic correlations between testing antigens and reference antisera in each individual experiment. Thus, we have to combine and integrate the HI tables from a number of individual experiments.

Visual classification with multitask joint sparse representation.

We address the problem of visual classification with multiple features and/or multiple instances. Motivated by the recent success of multitask joint covariate selection, we formulate this problem as a multitask joint sparse representation model to combine the strength of multiple features and/or instances for recognition. A joint sparsity-inducing norm is utilized to enforce class-level joint sparsity patterns among the multiple representation vectors.

Nondegenerate piecewise linear systems: a finite Newton algorithm and applications in machine learning.

We investigate Newton-type optimization methods for solving piecewise linear systems (PLSs) with nondegenerate coefficient matrix. Such systems arise, for example, from the numerical solution of linear complementarity problem, which is useful to model several learning and optimization problems. In this letter, we propose an effective damped Newton method, PLS-DN, to find the exact (up to machine precision) solution of nondegenerate PLSs.