microRNA在亨廷顿病中的研究进展

亨廷顿病(Huntington's disease, HD)是神经退行性疾病(neurodegenerative disorders)中一种单基因遗传病,由亨廷顿基因(the huntingtin gene, HTT)第一个外显子中的CAG三核苷酸序列重复扩增引起,尚无法治愈。HTT编码的蛋白产物被称为亨廷顿蛋白(Huntingtin protein, Htt)。突变亨廷顿蛋白(mutated huntingtin protein, mHtt)容易形成聚集体,具有毒性,导致一系列细胞学异常和神经元功能障碍。microRNA(miRNA)在基因转录后水平调控中起重要作用,其表达的改变与HD病理过程有关,成为治疗HD的潜在生物标志物。近年来,对一些特定miRNA在HD中调控机制及靶基因预测方面的研究可为HD提供潜在的治疗方法。本文就miRNA在HD中的相关研究进展进行综述。     

Pt–Ru nanoparticles anchored on poly(brilliant cresyl blue) as a new polymeric support: Application as an efficient electrocatalyst in methanol oxidation reaction

The glassy carbon electrode is modified by poly(brilliant cresyl blue) (PBCB) to be applied as a new green and efficient platform for Pt and Pt–Ru alloy nanoparticles deposition. Surface composition, morphology and catalytic activity of these modified electrodes towards methanol oxidation are assessed by applying X-ray diffraction, field emission scanning electron microscopy, cyclic voltammetry and electrochemical impedance spectroscopy techniques. The X-ray diffraction patterns reveal that the highly crystalline Pt and Pt–Ru alloy and RuO₂ nanoparticles with low crystallinity are deposited on the PBCB modified glassy carbon electrodes. The microscopic images indicate smaller size and better distribution of deposited nanoparticles on the surface of PBCB modified electrodes. Cyclic voltammetry and electrochemical impedance spectroscopy results reveal that PBCB supported Pt and Pt–Ru nanoparticles have better electrocatalytic performance and durability towards methanol oxidation rather than the unsupported nanoparticles. From the obtained results it can be concluded that the presence of PBCB not only improves the stability of nanoparticles on the surface, but also leads to the formation of smaller size and more uniform distribution of nanoparticles on the surface, which, in turn, cause the nanoparticles to provide a higher accessible surface area and more active centers for the oxidation of methanol. The results will be valuable in extending the applications of this polymer in surface modification steps and in developing promising catalyst supports to be applied in direct methanol fuel cells.     

Impact sensitivities of energetic materials: Exploring the limitations of a model based only on structural formulas

Using a comprehensive set of drop weight impact test data (h₅₀) newly compiled from literature for 308 materials, a recent approach to predict impact sensitivities of nitro compounds is generalized to most explosive substances of interest. Compared to previous ones, this procedure is more thoroughly validated and exhibits a good predictive value. Furthermore, it yields new insight into the physical mechanisms involved, explaining for instance the unexpected desensitization of some oxygen-deficient triazoles upon nitration.     

Homogenization with uncertainty in Poisson ratio for polymers with rubber particles

The main aim of this work is dual computer analysis of probabilistic coefficients for the homogenized tensor of the polymer filled with the rubber particles having randomized Poisson ratios of both constituents. The major issue is to verify an influence of a randomness in rubber Poisson ratio close to the compressibility limit on the uncertainty of the effective tensor probabilistic characteristics. Probabilistic analysis presented here is carried out using mainly the stochastic perturbation technique provided by the common application of the traditional FEM commercial code ABAQUS and the symbolic computations package MAPLE. This FEM-based technique employs polynomial response function of the optimum order recovered from the weighted least squares method and following a set of deterministic solutions obtained for various values of the randomized input parameter. Optimization procedure is released entirely into a symbolic environment, where maximization of the correlation factor together with minimization of the fitting variance and approximation error are applied. Homogenization technique consists in equating of deformation energies for the real composite and the artificial one characterized by the effective elasticity tensor with uncertainty.     

Ultrasmall rhodium nanoclusters anchored on nitrogen-doped carbon nanotubes with embedded nickel nanoparticles as magnetically recyclable catalysts for efficient ammonia-borane hydrolysis

Facile yet efficient synthesis of high-performance nanocatalysts for hydrogen evolution from ammonia-borane (AB) hydrolysis is paramount. Here, we reported a novel hybrid nanocatalyst comprised of Rh nanoclusters (1.56 nm in diameters) anchored on nitrogen (N)-doped carbon nanotubes with embedded Ni nanoparticles (Ni@NCNTs), which was fabricated through adsorption of Rh ions on Ni@NCNTs. The achieved hybrid of Rh/Ni@NCNTs displayed excellent catalytic property (Turnover frequency: 959 min⁻¹) toward AB hydrolysis, higher than many prior developed Rh-based catalysts. Note that this hybrid could be reused for at least nine runs with complete AB conversion to hydrogen. Rh nanoclusters with small size exhibiting high atom utilization and the synergetic effect between Ni and Rh are responsible for the excellent catalytic property of Rh/Ni@NCNTs toward AB hydrolysis. This work highlights the importance of utilization of magnetically recyclable Ni@NCNTs as support and synergetic component for efficient hydrolysis of AB.     

Cost-minimization analysis of a working vacation queue with N-policy and server breakdowns

This paper investigates the N-policy M/M/1 queueing system with working vacation and server breakdowns. As soon as the system becomes empty, the server begins a working vacation. The server works at a lower service rate rather than completely stopping service during a vacation period. The server may break down with different breakdown rates during the idle, working vacation, and normal busy periods. It is assumed that service times, vacation times, and repair times are all exponentially distributed. We analyze this queueing model as a quasi-birth–death process. Furthermore, the equilibrium condition of the system is derived for the steady state. Using the matrix-geometric method, we find the matrix-form expressions for the stationary probability distribution of the number of customers in the system and system performance measures. The expected cost function per unit time is constructed to determine the optimal values of the system decision variables, including the threshold N and mean service rates. We employ the particle swarm optimization algorithm to solve the optimization problem. Finally, numerical results are provided, and an application example is given to demonstrate the applicability of the queueing model.     

Mixed-Metal MOF-74 Templated Catalysts for Efficient Carbon Dioxide Capture and Methanation

The vast chemical and structural tunability of metal–organic frameworks (MOFs) are beginning to be harnessed as functional supports for catalytic nanoparticles spanning a range of applications. However, a lack of straightforward methods for producing nanoparticle-encapsulated MOFs as efficient heterogeneous catalysts limits their usage. Herein, a mixed-metal MOF, NiMg-MOF-74, is utilized as a template to disperse small Ni nanoclusters throughout the parent MOF. By exploiting the difference in Ni O and Mg O coordination bond strength, Ni²⁺ is selectively reduced to form highly dispersed Ni nanoclusters constrained by the parent MOF pore diameter, while Mg²⁺ remains coordinated in the framework. By varying the ratio of Ni to Mg in the parent MOF, accessible surface area and crystallinity can be tuned upon thermal treatment, influencing CO₂ adsorption capacity and hydrogenation selectivity. The resulting Ni nanoclusters prove to be an active catalyst for CO₂ methanation and are examined using extended X-ray absorption fine structure and X-ray photoelectron spectroscopy. By preserving a segment of the Mg²⁺-containing MOF framework, the composite system retains a portion of its CO₂ adsorption capacity while continuing to deliver catalytic activity. The approach is thus critical for designing materials that can bridge the gap between carbon capture and CO₂ utilization.     

Length–weight relationships of five catfish species (Siluriformes) from the Ganga River,India

Knowledge of the length–weight relationships (LWRs) of fish is an important tool to understand fish body form, growth pattern, stock management and their conservation. The present study focused on investigating the length–weight relationships for five catfish species, Pachypterus atherinoides (Bloch, 1794), belonging to family Horabagridae; Batasio batasio (Hamilton, 1822) family Bagridae; Bagarius yarrelli (Sykes, 1839), family Gogangra viridescens (Hamilton, 1822); and Sisor rhabdophorus (Hamilton, 1822) belonging to family Sisoridae. Specimens were collected from the middle stretch of the Ganga River in India from November 2016 to May 2018. A total of 174 specimens of five fish species were collected, and their total lengths were measured to the nearest centimetre and the body weight to the nearest gram. The value of the parameter slope (b) of LWRs of the five species ranged from 2.86 (B. yarrelli) to 3.16 (G. viridescens), with a mean value of 2.99. The results of the present study documented the new maximum total length (TL) for P. atherinoides and S. rhabdophorus. The present study also provides the first reference regarding LWRs for S. rhabdophorus.     

Atomistic insight into the hydration and proton conducting mechanisms of the cobalt doped Ruddlesden-Popper structure Sr3Fe2O7-δ

Sr₃Fe₂O_7-δ (SFO) with two-layer Ruddlesden-Popper (R–P) structure has recently been proved to be a promising material for the single phase cathode in proton conducting solid oxide fuel cells (P–SOFCs). To investigate the hydration reactions and proton conducting mechanisms of SFO and cobalt doped SFO (SFCO), both bulk and surface properties were calculated. We conclude that R–P structures have advantages in P–SOFCs. The unique Sr–O–M layer can facilitate the hydration process. Although in Sr–O–F and Sr–O–N layers, it is difficult for the formation and migration of oxygen vacancies, protons are most stable. Furthermore, cobalt doping can not only improve the electronic conductivity but also enhance surface properties of SFCO. The easily exposed Co–Fe–O surface can also facilitate the hydration reactions on the surface. Our work could give an informative insight into the relationships among the doped elements, the R–P structures, the hydration process and the proton conducting properties.     

Sequence-Dependent Nanofiber Structures of Phenylalanine and Isoleucine Tripeptides

The molecular design of short peptides to achieve a tailor-made functional architecture has attracted attention during the past decade but remains challenging as a result of insufficient understanding of the relationship between peptide sequence and assembled supramolecular structures. We report a hybrid-resolution model to computationally explore the sequence–structure relationship of self-assembly for tripeptides containing only phenylalanine and isoleucine. We found that all these tripeptides have a tendency to assemble into nanofibers composed of laterally associated filaments. Molecular arrangements within the assemblies are diverse and vary depending on the sequences. This structural diversity originates from (1) distinct conformations of peptide building blocks that lead to different surface geometries of the filaments and (2) unique sidechain arrangements at the filament interfaces for each sequence. Many conformations are available for tripeptides in solution, but only an extended β-strand and another resembling a right-handed turn are observed in assemblies. It was found that the sequence dependence of these conformations and the packing of resulting filaments are determined by multiple competing noncovalent forces, with hydrophobic interactions involving Phe being particularly important. The sequence pattern for each type of assembly conformation and packing has been identified. These results highlight the importance of the interplay between conformation, molecular packing, and sequences for determining detailed nanostructures of peptides and provide a detailed insight to support a more precise design of peptide-based nanomaterials.