Self-catalytic induced interstitial C-doping of Pd nanoalloys for highly selective electrocatalytic dehydrogenation of formic acid

Light-metalloid-atom-doped Pd interstitial nanoalloy is promising candidate for electrocatalysis because of the favorable electronic effect.Herein,an innovative method was developed to synthesize C-doped Pd interstitial nanoalloy using palladium acetate both as metal precursor and C dopant.Elaborate characterizations demonstrated that C atoms were successfully doped into the Pd lattice via self-catalytic decomposition of acetate ions.The as-synthesized C-doped Pd catalysts showed excellent activity and durable stability for formic acid electrooxidation.The mass activity and specific activity at 0.6 V of C-doped Pd were approximately 2.59 A/mg and 3.50 mA cm^(-2),i.e.,2.4 and 2.6 times of Pd,respectively.DFT calculations revealed that interstitial doping with C atoms induced differentiation of Pd sites.The strong noncovalent interaction between the Pd sites and the key intermediates endowed Pd with high-selectivity to direct routes and enhanced CO tolerance.This work presents a sites-differentiation strategy for metallic catalysts to improve the electrocatalysis.

Mash1 efficiently reprograms rat astrocytes into neurons

To date, it remains poorly understood whether astrocytes can be easily reprogrammed into neurons. Mashl and Brn2 have been previously shown to cooperate to reprogram fibroblasts into neurons. In this study, we examined astrocytes from 2-month-old Sprague-Dawley rats, and found that Brn2 was expressed, but Mashl was not detectable. Thus, we hypothesized that Mashl alone could be used to reprogram astrocytes into neurons. We transfected a recombinant MSCV-MASH1 plasmid into astrocytes for 72 hours, and saw that all cells expressed Mashl. One week later, we observed the changes in morphology of astrocytes, which showed typical neuro- nal characteristics. Moreover, β-tubulin expression levels were significantly higher in astrocytes expressing Mashl than in control cells. These results indicate that Mashl alone can reprogram astrocytes into neurons.

Inhibition of Axinl in osteoblast precursor cells leads to defects in postnatal bone growth through suppressing osteoclast formation

Axinl is a negative regulator of β-catenin signaling and its role in osteoblast precursor cells remains undefined.In the present studies,we determined changes in postnatal bone growth by deletion of Axinl in osteoblast precursor cells and analyzed bone growth in newborn and postnatal Axin1 O5X mice and found that hypertrophic cartilage area was largely expanded in AxinlOSX KO mice.A larger number of chondrocytes and unabsorbed cartilage matrix were found in the bone marrow cavity of Axin1OSX KO mice.Osteoclast formation in metaphyseal and subchondral bone areas was significantly decreased,demonstrated by decreased TRAPpositive cell numbers,associated with reduction of MMP9-and cathepsin K-positive cell numbers in Axin1 O5X KO mice.OPG expression and the ratio of O p g to Rankl were significantly increased in osteoblasts of Axinl O5X KO mice.Osteoclast formation in primary bone marrow derived microphage(BMM)cells was significantly decreased when BMM cells were cultured with conditioned media(CM)collected from osteoblasts derived from Axin1OSX mice compared with BMM cells cultured with CM derived from WT mice.Thus,the loss of Axinl in osteoblast precursor cells caused increased OPG and the decrease in osteoclast formation,leading to delayed bone growth in postnatal Axin1°sx KO mice.

A robust extraction algorithm for biomedical signals from noisy mixtures

Blind source extraction （BSE） is widely used to solve signal mixture problems where there are only a few desired signals. To improve signal extraction performance and expand its application, we develop an adaptive BSE algorithm with an additive noise model. We first present an improved normalized kurtosis as an objective function, which caters for the effect of noise. By combining the objective function and Lagrange multiplier method, we further propose a robust algorithm that can extract the desired signal as the first output signal. Simulations on both synthetic and real biomedical signals demonstrate that such combination improves the extrac- tion performance and has better robustness to the estimation error of normalized kurtosis value in the presence of noise.

Noisy component extraction with reference

Blind source extraction （BSE） is particularly at- tractive to solve blind signal mixture problems where only a few source signals are desired. Many existing BSE methods do not take into account the existence of noise and can only work well in noise-free environments. In practice, the desired signal is often contaminated by additional noise. Therefore, we try to tackle the problem of noisy component extraction. The reference signal carries enough prior information to dis- tinguish the desired signal from signal mixtures. According to the useful properties of Gaussian moments, we incorporate the reference signal into a negentropy objective function so as to guide the extraction process and develop an improved BSE method. Extensive computer simulations demonstrate its validity in the process of revealing the underlying desired signal.