Modeling human gray hair by irradiation as a valuable tool to study aspects of tissue aging.

As one of the earliest and most visible phenomenon of aging, gray hair makes it a unique model system for investigating the mechanism of aging. Ionizing radiation successfully induces gray hair in mice, and also provides a venue to establish an organ-cultured human gray hair model. To establish a suitable organ-cultured human gray HF model by IR, which imitates gray hair in the elderly, and to explore the mechanisms behind the model. By detecting growth parameters, melanotic and senescence markers of the model, we found that the model of 5 Gy accords best with features of elderly gray hair. Then, we investigated the formation mechanisms of the model by RNA-sequencing. We demonstrated that the model of organ-cultured gray HFs after 5 Gy irradiation is closest to the older gray HFs. Moreover, the 5 Gy inhibited the expression of TRP-1, Tyr, Pmel17, and MITF in hair bulbs/ORS of HFs. The 5 Gy also significantly induced ectopically pigmented melanocytes and increased the expression of DNA damage and senescence in HFs. Finally, RNA-seq analysis of the model suggested that IR resulted in cell DNA damage, and the accumulation of oxidative stress in the keratinocytes. Oxidative stress and DNA damage caused cell dysfunction and decreased melanin synthesis in the gray HFs. We found that HFs irradiated at 5 Gy successfully constructed an appropriate aging HF model. This may provide a useful model for cost-effective and predictable treatment strategies to human hair graying and the process of aging.

Tuning proton-coupled electron transfer by crystal orientation for efficient water oxidization on double perovskite oxides.

Developing highly efficient and cost-effective oxygen evolution reaction (OER) electrocatalysts is critical for many energy devices. While regulating the proton-coupled electron transfer (PCET) process via introducing additive into the system has been reported effective in promoting OER activity, controlling the PCET process by tuning the intrinsic material properties remains a challenging task. In this work, we take double perovskite oxide PrBa0.5Sr0.5Co1.5Fe0.5O5+δ (PBSCF) as a model system to demonstrate enhancing OER activity through the promotion of PCET by tuning the crystal orientation and correlated proton diffusion. OER kinetics on PBSCF thin films with (100), (110), and (111) orientation, deposited on single crystal LaAlO3 substrates, were investigated using electrochemical measurements, density functional theory (DFT) calculations, and synchrotron-based near ambient X-ray photoelectron spectroscopy. The results clearly show that the OER activity and the ease of deprotonation depend on orientation and follow the order of (100) > (110) > (111). Correlated with OER activity, proton diffusion is found to be the fastest in the (100) film, followed by (110) and (111) films. Our results point out a way of boosting PCET and OER activity, which can also be successfully applied to a wide range of crucial applications in green energy and environment.

Uncovering the Effect of Lattice Strain and Oxygen Deficiency on Electrocatalytic Activity of Perovskite Cobaltite Thin Films.

Developing cost effective electrocatalysts with high oxygen evolution reaction (OER) activity is essential for large-scale application of many electrochemical energy systems. Although the impacts of either lattice strain or oxygen defects on the OER performance of oxide catalysts have been extensively investigated, the effects of both factors are normally treated separately. In this work, the coupled effects of both strain and oxygen deficiency on the electrocatalytic activity of La0.7Sr0.3CoO3-δ (LSC) thin films grown on single crystal substrates (LaAlO3 (LAO) and SrTiO3 (STO)) are investigated. Electrochemical tests show that the OER activities of LSC films are higher under compression than under tension, and are diminished as oxygen vacancies are introduced by vacuum annealing. Both experimental and computational results indicate that the LSC films under tension (e.g., LSC/STO) have larger oxygen deficiency than the films under compression (e.g., LSC/LAO), which attribute to smaller oxygen vacancy formation energy. Such strain-induced excessive oxygen vacancies in the LSC/STO increases the eg state occupancy and enlarges the energy gap between the O 2p and Co 3d band, resulting in lower OER activity. Understanding the critical role of strain-defect coupling is important for achieving the rational design of highly active and durable catalysts for energy devices.

Defect Engineering in Single-Layer MoS2 Using Heavy Ion Irradiation.

Transition metal dichalcogenides (TMDs) have attracted much attention due to their promising optical, electronic, magnetic, and catalytic properties. Engineering the defects in TMDs represents an effective way to achieve novel functionalities and superior performance of TMDs devices. However, it remains a significant challenge to create defects in TMDs in a controllable manner or to correlate the nature of defects with their functionalities. In this work, taking single-layer MoS2 as a model system, defects with controlled densities are generated by 500 keV Au irradiation with different ion fluences, and the generated defects are mostly S vacancies. We further show that the defects introduced by ion irradiation can significantly affect the properties of the single-layer MoS2, leading to considerable changes in its photoluminescence characteristics and electrocatalytic behavior. As the defect density increases, the characteristic photoluminescence peak of MoS2 first blueshifts and then redshifts, which is likely due to the electron transfer from MoS2 to the adsorbed O2 at the defect sites. The generation of the defects can also strongly improve the hydrogen evolution reaction activity of MoS2, attributed to the modified adsorption of atomic hydrogen at the defects.

Globally Optimal Distributed Kalman Filtering for Multisensor Systems with Unknown Inputs.

In this paper, the state estimation for dynamic system with unknown inputs modeled as an autoregressive AR (1) process is considered. We propose an optimal algorithm in mean square error sense by using difference method to eliminate the unknown inputs. Moreover, we consider the state estimation for multisensor dynamic systems with unknown inputs. It is proved that the distributed fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurement; therefore, it achieves the best performance. The computation complexity of the traditional augmented state algorithm increases with the augmented state dimension. While, the new algorithm shows good performance with much less computations compared to that of the traditional augmented state algorithms. Moreover, numerical examples show that the performances of the traditional algorithms greatly depend on the initial value of the unknown inputs, if the estimation of initial value of the unknown input is largely biased, the performances of the traditional algorithms become quite worse. However, the new algorithm still works well because it is independent of the initial value of the unknown input.

The Cramér-Rao Bounds and Sensor Selection for Nonlinear Systems with Uncertain Observations.

This paper considers the problems of the posterior Cramér-Rao bound and sensor selection for multi-sensor nonlinear systems with uncertain observations. In order to effectively overcome the difficulties caused by uncertainty, we investigate two methods to derive the posterior Cramér-Rao bound. The first method is based on the recursive formula of the Cramér-Rao bound and the Gaussian mixture model. Nevertheless, it needs to compute a complex integral based on the joint probability density function of the sensor measurements and the target state. The computation burden of this method is relatively high, especially in large sensor networks. Inspired by the idea of the expectation maximization algorithm, the second method is to introduce some 0-1 latent variables to deal with the Gaussian mixture model. Since the regular condition of the posterior Cramér-Rao bound is unsatisfied for the discrete uncertain system, we use some continuous variables to approximate the discrete latent variables. Then, a new Cramér-Rao bound can be achieved by a limiting process of the Cramér-Rao bound of the continuous system. It avoids the complex integral, which can reduce the computation burden. Based on the new posterior Cramér-Rao bound, the optimal solution of the sensor selection problem can be derived analytically. Thus, it can be used to deal with the sensor selection of a large-scale sensor networks. Two typical numerical examples verify the effectiveness of the proposed methods.

Structural Exfoliation of Layered Cathode under High Voltage and Its Suppression by Interface Film Derived from Electrolyte Additive.

Layered cathodes for lithium-ion battery, including LiCo1-x-yNixMnyO2 and xLi2MnO3·(1-x)LiMO2 (M = Mn, Ni, and Co), are attractive for large-scale applications such as electric vehicles, because they can deliver additional specific capacity when the end of charge voltage is improved to over 4.2 V. However, operation under a high voltage might cause capacity decaying of layered cathodes during cycling. The failure mechanisms that have been given, up to date, include the electrolyte oxidation decomposition, the Ni, Co, or Mn ion dissolution, and the phase transformation. In this work, we report a new mechanism involving the exfoliation of layered cathodes when the cathodes are performed with deep cycling under 4.5 V in the electrolyte consisting of carbonate solvents and LiPF6 salt. Additionally, an electrolyte additive that can form a cathode interface film is applied to suppress this exfoliation. A representative layered cathode, LiCoO2, and an interface film-forming additive, dimethyl phenylphosphonite (DMPP), are selected to demonstrate the exfoliation and the protection of layered structure. When evaluated in half-cells, LiCoO2 exhibits a capacity retention of 24% after 500 cycles in base electrolyte, but this value is improved to 73% in the DMPP-containing electrolyte. LiCoO2/graphite full cell using DMPP behaves better than the Li/LiCoO2 half-cell, delivering an initial energy density of 700 Wh kg -1 with an energy density retention of 82% after 100 cycles at 0.2 C between 3 and 4.5 V, as compared to 45% for the cell without using DMPP.

SCGPred: a score-based method for gene structure prediction by combining multiple sources of evidence.

Predicting protein-coding genes still remains a significant challenge. Although a variety of computational programs that use commonly machine learning methods have emerged, the accuracy of predictions remains a low level when implementing in large genomic sequences. Moreover, computational gene finding in newly sequenced genomes is especially a difficult task due to the absence of a training set of abundant validated genes. Here we present a new gene-finding program, SCGPred, to improve the accuracy of prediction by combining multiple sources of evidence. SCGPred can perform both supervised method in previously well-studied genomes and unsupervised one in novel genomes. By testing with datasets composed of large DNA sequences from human and a novel genome of Ustilago maydi, SCG-Pred gains a significant improvement in comparison to the popular ab initio gene predictors. We also demonstrate that SCGPred can significantly improve prediction in novel genomes by combining several foreign gene finders with similarity alignments, which is superior to other unsupervised methods. Therefore, SCG-Pred can serve as an alternative gene-finding tool for newly sequenced eukaryotic genomes. The program is freely available at http://bio.scu.edu.cn/SCGPred/.

Globally Optimal Multisensor Distributed Random Parameter Matrices Kalman Filtering Fusion with Applications.

This paper proposes a new distributed Kalman filtering fusion with random state transition and measurement matrices, i.e., random parameter matrices Kalman filtering. It is proved that under a mild condition the fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurements; therefore, it achieves the best performance. More importantly, this result can be applied to Kalman filtering with uncertain observations including the measurement with a false alarm probability as a special case, as well as, randomly variant dynamic systems with multiple models. Numerical examples are given which support our analysis and show significant performance loss of ignoring the randomness of the parameter matrices.