Boosting Activity and Selectivity of UiO-66 through Acidity/Alkalinity Functionalization in Dimethyl Carbonate Catalysis.

The acid-base properties of supports have an enormous impact on catalytic reactions to regulate the selectivity and activity of supported catalysts. Herein, a train of Pd-X-UiO-66 (X = NO2 , NH2 , and CH3 ) catalysts with different acidity/alkalinity functional groups and encapsulated Pd(II) species is first developed, whose activities in dimethyl carbonate (DMC) catalysis are then investigated in details. Thereinto, the Pd-NO2 -UiO-66 catalyst with acidity functionalization exhibits the best catalytic behavior: the DMC selectivity stemmed from methyl nitrite (MN) is up to 68%, the conversion of CO is 73.4%. The obtained experimental results demonstrate that the NO2 group not only affected the interaction between X-UiO-66 and Pd(II) active sites but also play an indispensable role in the adsorption and activation of MN and CO, which remarkably promote the formation of the COOCH3 * intermediate and DMC product.

Emerging catalysts for the ambient synthesis of ethylene glycol from CO2 and its derivatives.

Ethylene glycol (EG), a useful chemical raw material, has been widely applied in many aspects of modern society. The conventional preparation of ethylene glycol mainly uses the petroleum route at high temperatures and pressure. More and more approaches have been developed to synthesize EG from CO2 and its derivatives under mild conditions. In this review, the ambient synthesis of EG from thermocatalysis, photocatalysis, and electrocatalysis is highlighted. The coal-to-ethylene glycol technology, one of the typical thermal catalysis routes for EG preparation, is relatively mature. However, it still faces some problems to be solved in industrialization. The recent progress in the development of coal-to-ethylene glycol technology is introduced. The main focus is on how to realize the preparation of EG under mild conditions. The strategies include doping promoters, modification of supports, design of catalysts with special structures, etc. Furthermore, the emerging technological progress of photocatalytic and electrocatalytic ethylene glycol synthesis under ambient conditions is introduced. Compared with the thermal catalytic reaction, the reaction conditions are milder. However, there are still many problems in large-scale production. Finally, we propose future development issues and related prospects for the ambient synthesis of EG using different catalytic routes.

Ambient-pressure synthesis of ethylene glycol catalyzed by C60-buffered Cu/SiO2.

Bulk chemicals such as ethylene glycol (EG) can be industrially synthesized from either ethylene or syngas, but the latter undergoes a bottleneck reaction and requires high hydrogen pressures. We show that fullerene (exemplified by C60) can act as an electron buffer for a copper-silica catalyst (Cu/SiO2). Hydrogenation of dimethyl oxalate over a C60-Cu/SiO2 catalyst at ambient pressure and temperatures of 180° to 190°C had an EG yield of up to 98 ± 1%. In a kilogram-scale reaction, no deactivation of the catalyst was seen after 1000 hours. This mild route for the final step toward EG can be combined with the already-industrialized ambient reaction from syngas to the intermediate of dimethyl oxalate.

Boron Modified Bifunctional Cu/SiO2 Catalysts with Enhanced Metal Dispersion and Surface Acid Sites for Selective Hydrogenation of Dimethyl Oxalate to Ethylene Glycol and Ethanol.

Boron (B) promoter modified Cu/SiO2 bifunctional catalysts were synthesized by sol-gel method and used to produce ethylene glycol (EG) and ethanol (EtOH) through efficient hydrogenation of dimethyl oxalate (DMO). Experimental results showed that boron promoter could significantly improve the catalytic performance by improving the structural characteristics of the Cu/SiO2 catalysts. The optimized 2B-Cu/SiO2 catalyst exhibited excellent low temperature catalytic activity and long-term stability, maintaining the average EG selectivity (Sel.EG) of 95% at 190 °C, and maintaining the average EtOH selectivity (Sel.EtOH) of 88% at 260 °C, with no decrease even after reaction of 150 h, respectively. Characterization results revealed that doping with boron promoter could significantly increase the copper dispersion, enhance the metal-support interaction, maintain suitable Cu+/(Cu+ + Cu0) ratio, and diminish metallic copper particles during the hydrogenation of DMO. Thus, this work introduced a bifunctional boron promoter, which could not only improve the copper dispersion, reduce the formation of bulk copper oxide, but also properly enhance the acidity of the sample surface, so that the Cu/SiO2 sample could exhibit superior EG selectivity at low temperature, as well as improving the EtOH selectivity at high temperature.

PlantMirP2: An Accurate, Fast and Easy-To-Use Program for Plant Pre-miRNA and miRNA Prediction.

MicroRNAs (miRNAs) are a kind of short non-coding ribonucleic acid molecules that can regulate gene expression. The computational identification of plant miRNAs is of great significance to understanding biological functions. In our previous studies, we have put firstly forward and further developed a set of knowledge-based energy features to construct two plant pre-miRNA prediction tools (plantMirP and riceMirP). However, these two tools cannot be used for miRNA prediction from NGS (Next-Generation Sequencing) data. In addition, for further improving the prediction performance and accessibility, plantMirP2 has been developed. Based on the latest dataset, plantMirP2 achieves a promising performance: 0.9968 (Area Under Curve, AUC), 0.9754 (accuracy), 0.9675 (sensitivity) and 0.9876 (specificity). Additionally, the comparisons with other plant pre-miRNA tools show that plantMirP2 performs better. Finally, the webserver and stand-alone version of plantMirP2 are available.

Enhanced logical chaotic resonance.

It was demonstrated recently that logical chaotic resonance (LCR) can be observed in a bistable system. In other words, the system can operate robustly as a specific logic gate in an optimal window of chaotic signal intensity. Here, we report that the size of the optimal window of chaotic signal intensity can be remarkably extended by exploiting the constructive interaction of chaotic signal and periodic force, as well as coupling, in a coupled bistable system. In addition, medium-frequency periodic force and an increasing system size can also lead to an improvement in the response speed of logic devices. The results are corroborated by circuit experiments. Taken together, a reliable and rapid-response logic operation can be realized based on periodic force- and array-enhanced LCR.

Periodic and aperiodic force-induced logical stochastic resonance in a bistable system.

It was demonstrated recently that there are optimal windows of noise intensity or frequency and amplitude of the periodic driving force, which let a bistable system operate reliably as logic gates. These phenomena are called logical stochastic resonance (LSR). Given that the driving force is not always perfect regular, there may be phase disturbance in driving force; therefore, the Wiener process is used here to model phase disturbance of driving force, and then the effects of phase disturbance on reliability and agility of logic gates are explored in detail. Comparing with the periodic force, the aperiodic force with appropriate intensity phase disturbance can drive a bistable system to yield phenomena similar to LSR in a wider reliable region and can reduce mean switching time to obtain a faster response of logic devices to the input signal. On the other hand, depending on the amplitude and average angular frequency, moderate-intensity phase disturbance may also reduce success probability and increase mean switching time and thus lead to the instability and the slower response of logic devices.

PlantMirP-Rice: An Efficient Program for Rice Pre-miRNA Prediction.

Rice microRNAs (miRNAs) are important post-transcriptional regulation factors and play vital roles in many biological processes, such as growth, development, and stress resistance. Identification of these molecules is the basis of dissecting their regulatory functions. Various machine learning techniques have been developed to identify precursor miRNAs (pre-miRNAs). However, no tool is implemented specifically for rice pre-miRNAs. This study aims at improving prediction performance of rice pre-miRNAs by constructing novel features with high discriminatory power and developing a training model with species-specific data. PlantMirP-rice, a stand-alone random forest-based miRNA prediction tool, achieves a promising accuracy of 93.48% based on independent (unseen) rice data. Comparisons with other competitive pre-miRNA prediction methods demonstrate that plantMirP-rice performs better than existing tools for rice and other plant pre-miRNA classification.

Set-reset latch logic operation in a bistable system under suprathreshold and subthreshold signals.

A set-reset latch is a basic building block of computers and can be used to store state information. Here, by testing the influence of the two logical input signals on the reliable set-reset latch logic operation in the bistable system, we found that there are two types of input signals, namely, suprathreshold and subthreshold signals. For the suprathreshold signals, reliable set-reset logic operation can be achieved without any driving forces and exhibits certain anti-interference ability; for the subthreshold signals, a single harmonic could induce correct set-reset latch logic operation but with a narrow optimal parameter region. The introduction of biharmonic-induced set-reset latch logic operation (logical vibrational resonance) could greatly expand the parameter region. Explanations for the above results were provided by taking the logical inputs as the dynamic bias to analyze the dynamic changes in the system. Finally, the results were further verified by circuit simulation and actual hardware circuit.

milRNApredictor: Genome-free prediction of fungi milRNAs by incorporating k-mer scheme and distance-dependent pair potential.

MicroRNA-like small RNAs (milRNAs) with length of 21-22 nucleotides are a type of small non-coding RNAs that are firstly found in Neurospora crassa in 2010. Identifying milRNAs of species without genomic information is a difficult problem. Here, knowledge-based energy features are developed to identify milRNAs by tactfully incorporating k-mer scheme and distance-dependent pair potential. Compared with k-mer scheme, features developed here can alleviate the inherent curse of dimensionality in k-scheme once k becomes large. In addition, milRNApredictor built on novel features performs comparably to k-mer scheme, and achieves sensitivity of 74.21%, and specificity of 75.72% based on 10-fold cross-validation. Furthermore, for novel miRNA prediction, there exists high overlap of results from milRNApredictor and state-of-the-art mirnovo. However, milRNApredictor is simpler to use with reduced requirements of input data and dependencies. Taken together, milRNApredictor can be used to de novo identify fungi milRNAs and other very short small RNAs of non-model organisms.

CO2 hydrogenation to high-value products via heterogeneous catalysis.

Recently, carbon dioxide capture and conversion, along with hydrogen from renewable resources, provide an alternative approach to synthesis of useful fuels and chemicals. People are increasingly interested in developing innovative carbon dioxide hydrogenation catalysts, and the pace of progress in this area is accelerating. Accordingly, this perspective presents current state of the art and outlook in synthesis of light olefins, dimethyl ether, liquid fuels, and alcohols through two leading hydrogenation mechanisms: methanol reaction and Fischer-Tropsch based carbon dioxide hydrogenation. The future research directions for developing new heterogeneous catalysts with transformational technologies, including 3D printing and artificial intelligence, are provided.

ANDIS: an atomic angle- and distance-dependent statistical potential for protein structure quality assessment.

BACKGROUND: The knowledge-based statistical potential has been widely used in protein structure modeling and model quality assessment. They are commonly evaluated based on their abilities of native recognition as well as decoy discrimination. However, these two aspects are found to be mutually exclusive in many statistical potentials. RESULTS: We developed an atomic ANgle- and DIStance-dependent (ANDIS) statistical potential for protein structure quality assessment with distance cutoff being a tunable parameter. When distance cutoff is ≤9.0 Å, "effective atomic interaction" is employed to enhance the ability of native recognition. For a distance cutoff of ≥10 Å, the distance-dependent atom-pair potential with random-walk reference state is combined to strengthen the ability of decoy discrimination. Benchmark tests on 632 structural decoy sets from diverse sources demonstrate that ANDIS outperforms other state-of-the-art potentials in both native recognition and decoy discrimination. CONCLUSIONS: Distance cutoff is a crucial parameter for distance-dependent statistical potentials. A lower distance cutoff is better for native recognition, while a higher one is favorable for decoy discrimination. The ANDIS potential is freely available as a standalone application at http://qbp.hzau.edu.cn/ANDIS/ .

Weak periodic signal detection by sine-Wiener-noise-induced resonance in the FitzHugh-Nagumo neuron.

Based on the FitzHugh-Nagumo (FHN) neuron model subjected to sine-Wiener (SW) noise, impacts of SW noise on weak periodic signal detection are investigated by calculating response measure Q for characterizing synchronization between the input signal and the output temporal activities of the neuron. It is numerically demonstrated that the response measure Q can achieve the optimal value under appropriate and moderate intensity or correlation time of SW noise, suggesting the occurrence of SW-noise-induced stochastic resonance. Furthermore, the optimal value of Q is sensitive to correlation time. Consequently, the correlation time of SW noise has a great influence on the performance of signal detection in the FHN neuron.

Diverse effects of distance cutoff and residue interval on the performance of distance-dependent atom-pair potential in protein structure prediction.

BACKGROUND: As one of the most successful knowledge-based energy functions, the distance-dependent atom-pair potential is widely used in all aspects of protein structure prediction, including conformational search, model refinement, and model assessment. During the last two decades, great efforts have been made to improve the reference state of the potential, while other factors that also strongly affect the performance of the potential have been relatively less investigated. RESULTS: Based on different distance cutoffs (from 5 to 22 Å) and residue intervals (from 0 to 15) as well as six different reference states, we constructed a series of distance-dependent atom-pair potentials and tested them on several groups of structural decoy sets collected from diverse sources. A comprehensive investigation has been performed to clarify the effects of distance cutoff and residue interval on the potential's performance. Our results provide a new perspective as well as a practical guidance for optimizing distance-dependent statistical potentials. CONCLUSIONS: The optimal distance cutoff and residue interval are highly related with the reference state that the potential is based on, the measurements of the potential's performance, and the decoy sets that the potential is applied to. The performance of distance-dependent statistical potential can be significantly improved when the best statistical parameters for the specific application environment are adopted.

Dynamics and robustness of the cardiac progenitor cell induced pluripotent stem cell network during cell phenotypes transition.

Robustness is a fundamental characteristic of biological systems since all living systems need to adapt to internal or external perturbations, unpredictable environments, stochastic events and unreliable components, and so on. A long-term challenge in systems biology is to reveal the origin of robustness underlying molecular regulator network. In this study, a simple Boolean model is used to investigate the global dynamic properties and robustness of cardiac progenitor cell (CPC) induced pluripotent stem cell network that governs reprogramming and directed differentiation process. It is demonstrated that two major attractors correspond to source and target cell phenotypes, respectively, and two dominating attracting trajectories characterise the biological pathways between two major cell phenotypes. In particular, the experimentally observed transition between different cell phenotypes can be reproduced and explained theoretically. Furthermore, the robustness of major attractors and trajectories is largely maintained with respect to small perturbations to the network. Taken together, the CPC-induced pluripotent stem cell network is extremely robustly designed for their functions.

Impact of bounded noise on the formation and instability of spiral wave in a 2D Lattice of neurons.

Spiral waves in the neocortex may provide a spatial framework to organize cortical oscillations, thus help signal communication. However, noise influences spiral wave. Many previous theoretical studies about noise mainly focus on unbounded Gaussian noise, which contradicts that a real physical quantity is always bounded. Furthermore, non-Gaussian noise is also important for dynamical behaviors of excitable media. Nevertheless, there are no results concerning the effect of bounded noise on spiral wave till now. Based on Hodgkin-Huxley neuron model subjected to bounded noise with the form of Asin[ωt + σW(t)], the influences of bounded noise on the formation and instability of spiral wave in a two-dimensional (2D) square lattice of neurons are investigated in detail by separately adjusting the intensity σ, amplitude A, and frequency f of bounded noise. It is found that the increased intensity σ can facilitate the formation of spiral wave while the increased amplitude A tends to destroy spiral wave. Furthermore, frequency of bounded noise has the effect of facilitation or inhibition on pattern synchronization. Interestingly, for the appropriate intensity, amplitude and frequency can separately induce resonance-like phenomenon.

Impact of Bounded Noise and Rewiring on the Formation and Instability of Spiral Waves in a Small-World Network of Hodgkin-Huxley Neurons.

Spiral waves are observed in the chemical, physical and biological systems, and the emergence of spiral waves in cardiac tissue is linked to some diseases such as heart ventricular fibrillation and epilepsy; thus it has importance in theoretical studies and potential medical applications. Noise is inevitable in neuronal systems and can change the electrical activities of neuron in different ways. Many previous theoretical studies about the impacts of noise on spiral waves focus an unbounded Gaussian noise and even colored noise. In this paper, the impacts of bounded noise and rewiring of network on the formation and instability of spiral waves are discussed in small-world (SW) network of Hodgkin-Huxley (HH) neurons through numerical simulations, and possible statistical analysis will be carried out. Firstly, we present SW network of HH neurons subjected to bounded noise. Then, it is numerically demonstrated that bounded noise with proper intensity σ, amplitude A, or frequency f can facilitate the formation of spiral waves when rewiring probability p is below certain thresholds. In other words, bounded noise-induced resonant behavior can occur in the SW network of neurons. In addition, rewiring probability p always impairs spiral waves, while spiral waves are confirmed to be robust for small p, thus shortcut-induced phase transition of spiral wave with the increase of p is induced. Furthermore, statistical factors of synchronization are calculated to discern the phase transition of spatial pattern, and it is confirmed that larger factor of synchronization is approached with increasing of rewiring probability p, and the stability of spiral wave is destroyed.

Noise Decomposition Principle in a Coherent Feed-Forward Transcriptional Regulatory Loop.

Coherent feed-forward loops exist extensively in realistic biological regulatory systems, and are common signaling motifs. Here, we study the characteristics and the propagation mechanism of the output noise in a coherent feed-forward transcriptional regulatory loop that can be divided into a main road and branch. Using the linear noise approximation, we derive analytical formulae for the total noise of the full loop, the noise of the branch, and the noise of the main road, which are verified by the Gillespie algorithm. Importantly, we find that (i) compared with the branch motif or the main road motif, the full motif can effectively attenuate the output noise level; (ii) there is a transition point of system state such that the noise of the main road is dominated when the underlying system is below this point, whereas the noise of the branch is dominated when the system is beyond the point. The entire analysis reveals the mechanism of how the noise is generated and propagated in a simple yet representative signaling module.

qiRNApredictor: A Novel Computational Program for the Prediction of qiRNAs in Neurospora crassa.

Recently, a new type of small interfering RNAs (qiRNAs) of typically 20~21 nucleotides was found in Neurospora crassa and rice and has been shown to regulate gene silencing in the DNA damage response. Identification of qiRNAs is fundamental for dissecting regulatory functions and molecular mechanisms. In contrast to other expensive and time-consuming experimental methods, the computational prediction of qiRNAs is a conveniently rapid method for gaining valuable information for a subsequent experimental verification. However, no tool existed to date for the prediction of qiRNAs. To this purpose, we developed the novel qiRNA prediction software package qiRNApredictor. This software demonstrates a promising sensitivity of 93.55% and a specificity of 71.61% from the leave-one-out validation. These studies might be beneficial for further experimental investigation. Furthermore, the local package of qiRNApredictor was implemented and made freely available to the academic community at Supplementary material.

plantMirP: an efficient computational program for the prediction of plant pre-miRNA by incorporating knowledge-based energy features.

MicroRNAs are a predominant type of small non-coding RNAs approximately 21 nucleotides in length that play an essential role at the post-transcriptional level by either RNA degradation, translational repression or both through an RNA-induced silencing complex. Identification of these molecules can aid the dissecting of their regulatory functions. The secondary structures of plant pre-miRNAs are much more complex than those of animal pre-miRNAs. In contrast to prediction tools for animal pre-miRNAs, much less effort has been contributed to plant pre-miRNAs. In this study, a set of novel knowledge-based energy features that has very high discriminatory power is proposed and incorporated with the existing features for specifically distinguishing the hairpins of real/pseudo plant pre-miRNAs. A promising performance area under a receiver operating characteristic curve of 0.9444 indicates that 5 knowledge-based energy features have very high discriminatory power. The 10-fold cross-validation result demonstrates that plantMirP with full features has a promising sensitivity of 92.61% and a specificity of 98.88%. Based on various different datasets, it was found that plantMirP has a higher prediction performance by comparison with miPlantPreMat, PlantMiRNAPred, triplet-SVM, and microPred. Meanwhile, plantMirP can greatly balance sensitivity and specificity for real/pseudo plant pre-miRNAs. Taken together, we developed a promising SVM-based program, plantMirP, for predicting plant pre-miRNAs by incorporating knowledge-based energy features. This study shows it to be a valuable tool for miRNA-related studies.