Theoretical study of N<Subscript>4</Subscript>X (X = O,S, Se) systems

A series of N₄X (X = O, S, Se) compounds have been examined with ab initio and density functional theory (DFT) methods. To our knowledge, these compounds, except for the C_2v ring and the C_3v towerlike isomers of N₄O, are first reported here. The ring structures are the most energetically favored for N₄X (X = O and S) systems. For N₄Se, the cagelike structure is the most energetically favored. Several decomposition and isomerization pathways for the N₄X species have been investigated. The dissociation of C_2v ring N₄O and N₄S structures via ring breaking and the barrier height are only 1.1 and −0.2 kcal mol⁻¹ at the CCSD(T)/6-311+G*//MP2/6-311+G* level of theory. The dissociation of the cagelike N₄X species is at a cost of 12.1–16.2 kcal mol⁻¹. As for the towerlike and triangle bipyramidal isomers, their decomposition or isomerization barrier heights are all lower than 10.0 kcal mol⁻¹. Although the C_S cagelike N₄S isomer has a moderate isomerization barrier (18.3–29.1 kcal mol⁻¹), the low dissociation barrier (−1.0 kcal mol⁻¹) indicates that it will disappear when going to the higher CCSD(T) level. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Sequence analysis and rule development of predicting protein stability change upon mutation using decision tree model

Understanding the mechanism of the protein stability change is one of the most challenging tasks. Recently, the prediction of protein stability change affected by single point mutations has become an interesting topic in molecular biology. However, it is desirable to further acquire knowledge from large databases to provide new insights into the nature of them. This paper presents an interpretable prediction tree method (named iPTREE-2) that can accurately predict changes of protein stability upon mutations from sequence based information and analyze sequence characteristics from the viewpoint of composition and order. Therefore, iPTREE-2 based on a regression tree algorithm exhibits the ability of finding important factors and developing rules for the purpose of data mining. On a dataset of 1859 different single point mutations from thermodynamic database, ProTherm, iPTREE-2 yields a correlation coefficient of 0.70 between predicted and experimental values. In the task of data mining, detailed analysis of sequences reveals the possibility of the compositional specificity of residues in different ranges of stability change and implies the existence of certain patterns. As building rules, we found that the mutation residues in wild type and in mutant protein play an important role. The present study demonstrates that iPTREE-2 can serve the purpose of predicting protein stability change, especially when one requires more understandable knowledge.     

Calcium alginate bead immobilization of cells containing tyrosine ammonia lyase activity for use in the production of p-hydroxycinnamic acid

An Escherichia coli catalyst with tyrosine ammonia lyase activity (TAL) has been stabilized for repeated use in batch conversions of high tyrosine solids to p-hydroxycinnamic acid (pHCA). The TAL biocatalyst was stabilized by controlling the reaction pH to 9.8 +/- 0.1 and immobilizing the cells within a calcium alginate matrix that was cross-linked with glutaraldehyde and polyethyleneimine (GA/PEI). We found a GA range where the bead-encapsulated TAL was not inactivated, and the resulting cross-linking provided the beads with the mechanical stability necessary for repeated use in consecutive batch reactions with catalyst recycle. The GA/PEI calcium alginate TAL catalyst was used in 41 1-L batch reactions where 50 g L(-1) tyrosine was converted to 39 +/- 4 g L(-1) pHCA in each batch. The practical usefulness and ease of this process was demonstrated by scaling up the TAL bead immobilization and using the immobilized TAL catalyst in four 125-L bioconversion reactions to produce over 12 kg of purified pHCA.     

Analysis of Biomolecular Interaction Process Based on SPR Imaging Method in Microfluidic Chips

Plasmonics - Molecular dynamics characteristics have important significance in studying the interaction between biomolecules, such as drug screening, environmental monitoring and evaluation of...     

Distribution characteristics of ammonia-oxidizing microorganisms and their responses to external nitrogen and carbon in sediments of a freshwater reservoir,China

Aquatic Ecology - Ammonia oxidation driven by ammonia-oxidizing archaea (AOA) and bacteria (AOB) plays a significant role in the nitrogen cycle, but the mechanism of this important&nbsp;action...     

Cost-aware job scheduling for cloud instances using deep reinforcement learning

As the services provided by cloud vendors are providing better performance, achieving auto-scaling, load-balancing, and optimized performance along with low infrastructure maintenance, more and more companies migrate their services to the cloud. Since the cloud workload is dynamic and complex, scheduling the jobs submitted by users in an effective way is proving to be a challenging task. Although a lot of advanced job scheduling approaches have been proposed in the past years, almost all of them are designed to handle batch jobs rather than real-time workloads, such as that user requests are submitted at any time with any amount of numbers. In this work, we have proposed a Deep Reinforcement Learning (DRL) based job scheduler that dispatches the jobs in real time to tackle this problem. Specifically, we focus on scheduling user requests in such a way as to provide the quality of service (QoS) to the end-user along with a significant reduction of the cost spent on the execution of jobs on the virtual instances. We have implemented our method by Deep Q-learning Network (DQN) model, and our experimental results demonstrate that our approach can significantly outperform the commonly used real-time scheduling algorithms.

     

Simulated nitrogen deposition induces shifts in growth and resource-use strategies during range expansion of an invasive plant

Biological Invasions - An appropriate resource-use strategy contributes to the invasion success of exotic plants. During range expansion, population density and nitrogen availability are two...