耐药结核分枝杆菌潜伏-复发感染动物模型的研究进展

潜伏结核感染(latent tuberculosis infection,LTBI)复发是新发结核病的主要来源,其中耐药结核病所占比例较大,使耐药LTBI复发的防控成为结核病研究的重点。耐药结核分枝杆菌潜伏-复发感染动物模型是开展耐药结核病防控相关机制研究、抗耐药结核分枝杆菌药物和疫苗研究的基础。目前耐药结核分枝杆菌感染动物模型缺乏,而已有的结核分枝杆菌标准株H37Rv潜伏-复发感染模型存在缺陷,如小鼠模型的潜伏期荷菌量偏高、复发期变异大,而猴模型的潜伏期和复发期不可预测。模型的可控性差使其应用困难,且缺乏可用的免疫学评价指标,导致远期复发无法预测。因此,基于现有H37Rv潜伏-复发感染动物模型的制备方法,展望耐药结核分枝杆菌潜伏-复发感染动物模型可能存在的缺陷,通过选用新的抑菌剂和诱导剂,制备有稳定潜伏期、潜伏时长适中、复发起点和复发水平变异小的动物模型,是未来耐药结核分枝杆菌潜伏-复发感染动物模型研究的方向。     

一类正广义系统的能控性和能观性

对于广义系统的能控性和能观性的研究已经取得了很多成果,但对于正广义系统的研究,目前还处于起步阶段,需要进一步的研究．本文研究了一类正广义系统,通过Drazin逆给出了判别系统能控性和能观性的充分条件,并通过例子验证了本文结论的有效性．     

Shuttlebox avoidance in rhesus monkeys: effects on plasma cortisol and beta-endorphin

Groups of monkeys either extensively pretrained to avoid shocks in a shuttlebox or with minimal prior experience were compared for plasma cortisol and beta-endorphin levels immediately following: (1) an exposure to the box with no shock, (2) the box providing repeated inescapable shocks or (3) a re-exposure to the box, again with no shock presentation. Mere exposure to the unfamiliar box elevated plasma cortisol just as much as exposure + shock did when inexperienced monkeys were tested. However, animals with a history of previously successful shock avoidance showed smaller elevations when exposed to the box alone, than they did when inescapable shock was received. Plasma beta-endorphin levels following shuttlebox exposure showed only a sporadic pattern of elevations in either inexperienced or pretrained monkeys. However, levels of beta-endorphin as determined under control conditions in the home cage were lower in pretrained animals, as were plasma levels of cortisol. The results indicate that behavioral factors may effect plasma cortisol and beta-endorphin following both acute and chronic shuttlebox experience.     

Connecting the dots in ethology: applying network theory to understand neural and animal collectives

A major goal shared by neuroscience and collective behavior is to understand how dynamic interactions between individual elements give rise to behaviors in populations of neurons and animals, respectively. This goal has recently become within reach, thanks to techniques providing access to the connectivity and activity of neuronal ensembles as well as to behaviors among animal collectives. The next challenge using these datasets is to unravel network mechanisms generating population behaviors. This is aided by network theory, a field that studies structure–function relationships in interconnected systems. Here we review studies that have taken a network view on modern datasets to provide unique insights into individual and collective animal behaviors. Specifically, we focus on how analyzing signal propagation, controllability, symmetry, and geometry of networks can tame the complexity of collective system dynamics. These studies illustrate the potential of network theory to accelerate our understanding of behavior across ethological scales.     

CONTROLLABILITY OF NON-LINEAR MODELS OF INTERACTING BIOLOGICAL POPULATIONS

Inthispaper,theauthorstudiedthecontrollabilityforakindofinteractingbiologicalpopulations.whichwasconsideredasamodelonepredatortwopreysystems,theauthorformulatedandprovedthesufficientconditionsforthelocalcontrollabilityofthisdynamicalsystems.     

Controllability of non-linear biochemical systems

Mathematical methods of biochemical pathway analysis are rapidly maturing to a point where it is possible to provide objective rationale for the natural design of metabolic systems and where it is becoming feasible to manipulate these systems based on model predictions, for instance, with the goal of optimizing the yield of a desired microbial product. So far, theory-based metabolic optimization techniques have mostly been applied to steady-state conditions or the minimization of transition time, using either linear stoichiometric models or fully kinetic models within biochemical systems theory (BST). This article addresses the related problem of controllability, where the task is to steer a non-linear biochemical system, within a given time period, from an initial state to some target state, which may or may not be a steady state. For this purpose, BST models in S-system form are transformed into affine non-linear control systems, which are subjected to an exact feedback linearization that permits controllability through independent variables. The method is exemplified with a small glycolytic-glycogenolytic pathway that had been analyzed previously by several other authors in different contexts.