Analysis of low-abundance proteins using the proteomic reactor with pH fractionation

We developed a new method consisting of the proteomic reactor coupled with step pH fractionation for the analysis of low-abundance proteins from minute amount of sample. These new reactors were implemented using both SAX and SCX materials. The pH fractions from the SAX reactor provided higher peptide and protein identification than SCX reactor and conventional solution digestion. Interestingly, the physical characteristics (pI, molecular weight, missed cleavage site and grand average hydrophobicity (GRAVY) index, and number of acid and basic amino acid) of the peptides obtained from the SAX and SCX proteomic reactors are drastically different. Furthermore, nearly half of the peptides observed from the pH fractionations from the SAX reactor are of low abundance while only 22% low-abundance proteins are observed with conventional in-solution digestion following 2D LC-MS/MS analysis.     

TERFDA: Tensor Embedding RF Domain Adaptation for varying noise interference

Radio frequency machine learning (RFML) can be loosely termed as a field that machine learning (ML) and deep learning (DL) techniques to applications related to wireless communications. However, traditional RFML basically assume that the data of training set and test set are independent and identically distributed and only a large number of labeled data can train a classification model which can effectively classify test set data. In other words, without enough training samples, it is impossible to learn an automatic modulation classifier that performs well in varying noise interference environment. Feature-based transfer learning minimizes the distribution difference between historical modulated signal data and new data by learning similarity-maximizing feature spaces. Therefore, in this paper, Dynamic Distribution Adaptation (DDA) is adopted to address the above challenges. We propose a Tensor Embedding RF Domain Adaptation (TERFDA) approach, which learns the latent subspace of the tensors formed by the time–frequency maps of the signals, so that use the multi-dimensional domain information of the signals to jointly learn the shared feature subspace of the source domain and the target domain, then perform DDA in the shared subspace. The experimental results show that under the modulated signal data, compared with the state-of-the-art DA algorithm, TERFDA has less requirements on the number of samples and categories, and has superior performance for confrontation the varying noise interference between source domain and target domain.     

Short-wave instabilities on a vortex pair of unequal strength circulation ratio

The creation of vortex pairs occurs in a range of industries, including mixing, transport, and plastic moulding. In particular, vortex pairs are observed in the wake of aircraft, and are the cause of a significant hazard in the aviation industry. Instabilities, which grow on vortex pairs, have the potential to lead to enhanced dissipation, thus limiting this safety concern, in addition to enhancing mixing in chemical engineering industries. To date research has mostly considered instabilities growing on a vortex pair where each vortex has the same magnitude of circulation. However, in practice it is unusual to have an equal-strength vortex pair. This investigation is the first to consider the instability modes that may develop on a Lamb–Oseen vortex pair of arbitrary circulation ratio. We find a significant change in the growth rates of all instability modes reported previously for an equal-strength vortex pair. All simulations employ an accurate spectral-element method to discretise the domain coupled with a three-step time splitting scheme. A wide range of instability wavelengths is considered to ensure that all instability modes are captured. By identifying and enhancing the leading instability modes, we are able to enhance the dissipation of the vortex pair.     

Neuroevolution strategies for episodic reinforcement learning

Because of their convincing performance, there is a growing interest in using evolutionary algorithms for reinforcement learning. We propose learning of neural network policies by the covariance matrix adaptation evolution strategy (CMA-ES), a randomized variable-metric search algorithm for continuous optimization. We argue that this approach, which we refer to as CMA Neuroevolution Strategy (CMA-NeuroES), is ideally suited for reinforcement learning, in particular because it is based on ranking policies (and therefore robust against noise), efficiently detects correlations between parameters, and infers a search direction from scalar reinforcement signals. We evaluate the CMA-NeuroES on five different (Markovian and non-Markovian) variants of the common pole balancing problem. The results are compared to those described in a recent study covering several RL algorithms, and the CMA-NeuroES shows the overall best performance.     

An anisotropic mesh adaptation method for the finite element solution of heterogeneous anisotropic diffusion problems

Heterogeneous anisotropic diffusion problems arise in the various areas of science and engineering including plasma physics, petroleum engineering, and image processing. Standard numerical methods can produce spurious oscillations when they are used to solve those problems. A common approach to avoid this difficulty is to design a proper numerical scheme and/or a proper mesh so that the numerical solution validates the discrete counterpart (DMP) of the maximum principle satisfied by the continuous solution. A well known mesh condition for the DMP satisfaction by the linear finite element solution of isotropic diffusion problems is the non-obtuse angle condition that requires the dihedral angles of mesh elements to be non-obtuse. In this paper, a generalization of the condition, the so-called anisotropic non-obtuse angle condition, is developed for the finite element solution of heterogeneous anisotropic diffusion problems. The new condition is essentially the same as the existing one except that the dihedral angles are now measured in a metric depending on the diffusion matrix of the underlying problem. Several variants of the new condition are obtained. Based on one of them, two metric tensors for use in anisotropic mesh generation are developed to account for DMP satisfaction and the combination of DMP satisfaction and mesh adaptivity. Numerical examples are given to demonstrate the features of the linear finite element method for anisotropic meshes generated with the metric tensors.     

Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteins

Understanding of proteins adaptive to hypersaline environment and identifying them is a challenging task and would help to design stable proteins. Here, we have systematically analyzed the normalized amino acid compositions of 2121 halophilic and 2400 non-halophilic proteins. The results showed that halophilic protein contained more Asp at the expense of Lys, Ile, Cys and Met, fewer small and hydrophobic residues, and showed a large excess of acidic over basic amino acids. Then, we introduce a support vector machine method to discriminate the halophilic and non-halophilic proteins, by using a novel Pearson VII universal function based kernel. In the three validation check methods, it achieved an overall accuracy of 97.7%, 91.7% and 86.9% and outperformed other machine learning algorithms. We also address the influence of protein size on prediction accuracy and found the worse performance for small size proteins might be some significant residues (Cys and Lys) were missing in the proteins.     

Metal Ion-Driven Constitutional Adaptation in Dynamic Covalent C=C/C=N Organo-Metathesis

Knoevenagel barbiturate derivatives and imines are able to undergo efficient component recombination through dynamic covalent C=C/C=N organo-metathesis in absence of a catalyst. A [2×2] dynamic covalent library (DCL) containing two Knoevenagel derivatives Kn1 and Kn2 and two imines A1 and A2 has been established and its adaptive features in response to the addition of metal cations have been investigated. Addition of Cu(I) triflate as an effector, induces fast and remarkable constitutional selection of the DCL towards amplification of the Cu(I)- A2 complex and its agonist Kn1 . This adaptation process could be reversed by addition of neocuproine as a competitive Cu(I) ligand. Furthermore, separate addition of five other metal cations as input agents, i. e. Ag(I), Fe(II), Zn(II), Cu(II) and Li(I), led to the generation of cation-specific distribution patterns as outputs, showing the ability of the present DCL to recognize different effectors.     

Effects of synaptic depression and adaptation on spatiotemporal dynamics of an excitatory neuronal network

We analyze the spatiotemporal dynamics of a system of integro-differential equations that describes a one-dimensional excitatory neuronal network with synaptic depression and spike frequency adaptation. Physiologically suggestive forms are used for both types of negative feedback. We also consider the effects of employing two different types of firing rate function, a Heaviside step function and a piecewise linear function. We first derive conditions for the existence of traveling fronts and pulses in the case of a Heaviside step firing rate, and show that adaptation plays a relatively minor role in determining the characteristics of traveling waves. We then derive conditions for the existence and stability of stationary pulses or bumps, and show that a purely excitatory network with synaptic depression cannot support stable bumps. However, bumps do not exist in the presence of adaptation. Finally, in the case of a piecewise linear firing rate function, we show numerically that the network also supports self-sustained oscillations between an Up state and a Down state, in which a spatially localized oscillating core periodically emits pulses at each cycle.