Dynamic control of toxic natural product biosynthesis by an artificial regulatory circuit

To mimic the delicately regulated metabolism in nature for improved efficiency, artificial and customized regulatory components for dynamically controlling metabolic networks in multiple layers are essential in laboratory engineering. For this purpose, a novel regulatory component for controlling vanillin biosynthetic pathway was developed through directed evolution, which was responsive to both the product vanillin and substrate ferulic acid, with different capacities. This regulatory component facilitated pathway expression via dynamic control of the intracellular substrate and product concentrations. As vanillin is an antimicrobial compound, low pathway expression and vanillin formation levels enabled better cell growth at an early stage, and the product feedback-activated pathway expression at later stages significantly improved biosynthesis efficiency. This novel multiple-layer dynamic control was demonstrated effective in managing the trade-off between cell growth and production, leading to improved cell growth and vanillin production compared to the conventional or quorum-sensing promoter-controlled pathway. The multiple-layer dynamic control enabled by designed regulatory components responsive to multiple signals shows potential for wide applications in addition to the dynamic controls based on biosynthetic intermediate sensing and quorum sensing reported to date.     

Modeling of dynamical systems through deep learning

This review presents a modern perspective on dynamical systems in the context of current goals and open challenges. In particular, our review focuses on the key challenges of discovering dynamics from data and finding data-driven representations that make nonlinear systems amenable to linear analysis. We explore various challenges in modern dynamical systems, along with emerging techniques in data science and machine learning to tackle them. The two chief challenges are (1) nonlinear dynamics and (2) unknown or partially known dynamics. Machine learning is providing new and powerful techniques for both challenges. Dimensionality reduction methods are used for projecting dynamical methods in reduced form, and these methods perform computational efficiency on real-world data. Data-driven models drive to discover the governing equations and give laws of physics. The identification of dynamical systems through deep learning techniques succeeds in inferring physical systems. Machine learning provides advanced new and powerful algorithms for nonlinear dynamics. Advanced deep learning methods like autoencoders, recurrent neural networks, convolutional neural networks, and reinforcement learning are used in modeling of dynamical systems.     

Obtaining maximum muscle excitation for normalizing shoulder electromyography in dynamic contractions

Muscle specific maximal voluntary isometric contractions (MVIC) are commonly used to elicit reference amplitudes to normalize electromyographic signals (EMG). It has been questioned whether this is appropriate for normalizing EMG from dynamic contractions. This study compares EMG amplitude when shoulder muscle activity from dynamic contractions is normalized to isometric and isokinetic maximal excitation as well as a hybrid approach currently used in our laboratory. Anterior, middle and posterior deltoid, upper and lower trapezius, pectoralis major, latissimus dorsi and infraspinatus were monitored during (1) manually resisted MVICs, and (2) maximum voluntary dynamic concentric contractions (MVDC) on an isokinetic dynamometer. Dynamic contractions were performed (a) at 30°/s about the longitudinal, frontal and sagittal axes of the shoulder, and (b) during manual bi-rotation of a tilted wheel at 120°/s. EMG from the wheel task was normalized to the maximum excitation from (i) the muscle specific MVIC, (ii) from any MVIC (MVIC_ALL), (iii) for any MVDC, (iv) from any exertion (maximum experimental excitation, MEE). Mean EMG from the wheel task was up to 45% greater when normalized to muscle specific isometric contractions (method i) than when normalized to MEE (method iv). Seventy-five percent of MEE’s occurred during MVDCs. This study presents an 20 useful and effective process for obtaining the greatest excitation from the shoulder muscles when normalizing dynamic efforts.     

Sowing different mixtures in dry acidic grassland produced priority effects of varying strength

Several longer-term assembly studies on ex-arable land have found that species that arrive first at a disturbed site can play a key role in the further development of the community and that this priority effect influences aboveground productivity, species diversity and stability of the grassland communities that develop. Restoration of nutrient poor, species rich grasslands is often limited by seed dispersal as well as the accessibility of suitable microsites for establishment. Sowing species (i.e. creating priority effects for further assembly) may help overcome such dispersal barriers, but the potential of using priority effects for restoration has not been tested in this type of dry grassland. We tested the hypothesis that sowing two different seed mixtures used for dry acidic grassland restoration onto a sandy substrate (which formed an equivalent to a primary succession) would create priority effects, and that these priority effects would be sustained over a number of years. We followed community assembly and measured aboveground productivity for four years after sowing. We found that priority effects caused by sowing of differently diverse mixtures did also occur in dry acidic grassland habitat, but that how persistent they were over time depended on the response variable considered. Priority effects on species number were not as strong as found in previous ex-arable land studies, whereas priority effects for aboveground productivity were still visible after 4 years. In addition, functional composition of the community still reflected the composition of the seed mixtures 4 years later. Our results suggest that priority effects can occur in nutrient-poor dry acidic grassland but in contrast to more nutrient-rich sites the breadth of responses affected may not be as wide.     

马铃薯立枯丝核菌拮抗菌QHZ11的实时荧光定量PCR快速检测与应用

【背景】植物根际促生菌(plant growth-promoting rhizobacteria,PGPR)在根际的定殖是其发挥作用的基础,直观有效的跟踪技术和定量方法是研究PGPR在根际原位分布规律的重要工具。【目的】建立一种马铃薯黑痣病病原菌——立枯丝核菌拮抗菌QHZ11的实时荧光定量PCR快速检测体系,并检测拮抗菌QHZ11在马铃薯根际的动态变化。【方法】根据GenBank中登录的类芽孢杆菌及近源菌株gyrB基因序列差异筛选特异性引物,优化反应条件;通过盆栽试验对马铃薯根际拮抗菌进行快速检测。盆栽试验设3个处理,T1：对照(无菌水,CK);T2：QHZ11菌悬液灌土(QHZ11);T3：将功能菌在有机肥中进行二次固体发酵制成生物有机肥(BOF11)。【结果】筛选出拮抗菌QHZ11的专用引物为gyrB-F/gyrB-R;建立的拮抗菌QHZ11实时荧光定量PCR检测方法特异性好、灵敏度高且重复性较好,线性相关系数为0.999 8,检测组内变异系数均在1%以内,扩增效率为0.9,可检测出1×103?1×1010 copies/g-soil的拮抗菌,具有检出限低和扩增效率高的特点。盆栽试验结果发现,T3处理马铃薯根际拮抗菌QHZ11的数量从接种的第10天即高出T2处理一个数量级,并于马铃薯盛花期(接种后第60天)达到峰值,说明二次固体发酵增加了拮抗菌在根际土壤中的存活和繁殖。【结论】建立的实时荧光定量PCR快速检测体系灵敏、高效,可为研究拮抗菌在马铃薯根际原位分布以及与病原菌的互作方面提供便捷有效的方法。     

Fitting dynamic growth models of biological phenomena from sample observations through Gaussian diffusion processes

This paper addresses the building of stochastic models that adequately describe dynamic phenomena and, in particular, those that occur in the Biosciences. In this context, the empirical fitting of a Gaussian diffusion process from sample data of a dynamic growth phenomenon is considered. In order to do this, a methodology based on approximations to its mean and variance functions is presented. Finally, several applications based on simulated and real data have been carried out.     

Physicochemical study of the protein–liposome interactions: influence of liposome composition and concentration on protein binding

Abstract

The aim of the present study is to investigate the interactions between liposomes and proteins and to evaluate the role of liposomal lipid composition and concentration in the formation of protein corona. Liposomes composed of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) or hydrogenated soybean phosphatidylcholine (HSPC) with 1,2-dipalmitoyl-sn-glycero-3-phospho-(1′-rac-glycerol) (sodium salt) (DPPG), 1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethylene glycol)-3000] (DPPE-PEG 3000), cholesterol (CH) or mixtures of these lipids, were prepared at different concentrations by the thin-film hydration method. After liposomes were dispersed in HPLC-grade water and foetal bovine serum (FBS), their physicochemical characteristics, such as size, size distribution, and ζ-potential, were determined using dynamic and electrophoretic light scattering. Aggregation of DPPC, HSPC, DPPC:CH (9:1 molar ratio), and HSPC:CH (9:1 molar ratio) in FBS was observed. On the contrary, liposomes incorporating DPPG lipids and CH both in a molar ratio of 11% were found to be stable over time, while their size did not alter dramatically in biological medium. Liposomes containing CH and PEGylated lipids retain their size in the presence of serum as well as their physical stability. In addition, our results indicate that the protein binding depends on the presence of polyethylene glycol (PEG), CH, concentration and surface charge. In this paper, we introduce a new parameter, fraction of stealthiness (F_s), for investigating the extent of protein binding to liposomes. This parameter depends on the changes in size of liposomes after serum incubation, while liposomes have stealth properties when F_s is close to 1. Thus, we conclude that lipid composition and concentration affect the adsorption of proteins and the liposomal stabilization.     

Electrotactile stimulation on the tongue: Intensity perception,discrimination, and cross-modality estimation

Due to its high sensitivity and conductivity, electrotactile stimulation (ETS) on the tongue has proven to be a useful and technically convenient tool to substitute and/or augment sensory capabilities. However, most of its applications have only provided spatial attributes and little is known about (a) the ability of the tongue's sensory system to process electrical stimuli of varying magnitudes and (b) how modulation of ETS intensity affects subjects’ ability to decode stimulus intensity. We addressed these questions by quantifying: (1) the magnitude of the dynamic range (DR; maximal comfortable intensity/perception threshold) and its sensitivity to prolonged exposure; (2) subjects’ ability to perceive intensity changes; and (3) subjects’ ability to associate intensity with angular excursions of a protractor's handle. We found that the average DR (17 dB) was generally large in comparison with other tactile loci and of a relatively constant magnitude among subjects, even after prolonged exposure, despite a slight but significant upward drift (p < 0.001). Additionally, our results showed that as stimulus intensity increased, subjects’ ability to discriminate ETS stimuli of different intensities improved (p < 0.05) while estimation accuracy, in general, slightly decreased (increasing underestimation). These results suggest that higher ETS intensity may increase recruitment of rapidly adapting mechanoreceptor fibers, as these are specialized for coding stimulus differences rather than absolute intensities. Furthermore, our study revealed that the tongue's sensory system can effectively convey electrical stimuli despite minimal practice and when information transfer is limited by memory and DR drift.     

Analytical methods for detecting pesticide switches with evolution of pesticide resistance

After a pest develops resistance to a pesticide, switching between different unrelated pesticides is a common management option, but this raises the following questions: (1) What is the optimal frequency of pesticide use? (2) How do the frequencies of pesticide applications affect the evolution of pesticide resistance? (3) How can the time when the pest population reaches the economic injury level (EIL) be estimated and (4) how can the most efficient frequency of pesticide applications be determined? To address these questions, we have developed a novel pest population growth model incorporating the evolution of pesticide resistance and pulse spraying of pesticides. Moreover, three pesticide switching methods, threshold condition-guided, density-guided and EIL-guided, are modelled, to determine the best choice under different conditions with the overall aim of eradicating the pest or maintaining its population density below the EIL. Furthermore, the pest control outcomes based on those three pesticide switching methods are discussed. Our results suggest that either the density-guided or EIL-guided method is the optimal pesticide switching strategy, depending on the frequency (or period) of pesticide applications.