取样方式对大鼠皮瓣缺血再灌注损伤模型细胞因子测定的影响

目标:利用大鼠腹岛状皮瓣构建缺血再灌注损伤模型,评价距血管夹闭点近、中、远不同距离的位置取样是否会影响细胞因子的检测,为该模型实验确立一种合理的取样方式。方法:对术后5天的大鼠成活皮瓣按照近、中、远三个位置进行取样,分别测定各样品的4种细胞因子IL-1β,IL-6,TNF-α和EPO的表达量,并用配对T检验分析评价三种取样方式。结果:样品中IL-1β,IL-6,TNF-α和EPO在缺血再灌注组的表达水平较假手术组显著上调;同一细胞因子在同一皮瓣不同取样位置的表达量在统计学上无显著性差异。结论:取样位置并不影响细胞因子的测定,即可以从成活皮瓣的任意位置取样测定相应细胞因子,但是在同一批实验中不同皮瓣取样位置须一致。     

鸽子海马区位置细胞及其空间响应特性分析

位置细胞被认为是构成空间认知地图的基本单元,对于动物空间认知具有十分重要的作用.为了解析鸟类的空间认知神经机制,本文以鸽子(Columba)为模式动物,设计了4种目标明确程度不同的空间认知任务,利用12只鸽子海马区记录的位置细胞,从定性、定量和稳定性3个方面对比了有目标和无目标环境中位置细胞的空间响应特性.结果表明,有目标环境比无目标环境更易诱发出位置野,且位置野的稳定性随环境中目标明确程度的提高而增加.此外,鸽子海马区位置细胞一般具有多个位置野,平均每个位置细胞有2.7个位置野,同时记录的位置细胞位置野基本处于相近的位置上,大部分都有重叠.本文的研究成果将有助于深化对不同物种动物空间认知机制的理解,也将为进一步解析鸽子的三维空间认知和导航机制奠定基础.     

应用神经网络和多元回归技术预测森林产量

应用传统统计技术常会因样本小和测量数据不符某种分布而受到限制。本文评价一种前馈型神经网络算法以预测落叶阔叶林产量。另外,还介绍一种由定性变为定量的数据变换方法,以用相对小的样本建立多元回归预测模型。数据变换方法有助于改善多元回归模型的预测效果。在本实验的条件下,研究结果表明神经网络技术能够产生最好的预测效果.     

通过反向病毒载体,把大鼠生长激素基因引入小鼠戍纤维细胞-表达与调控

我们通过反向病毒DNA载体,将大鼠的生长激素基因引入培养的小鼠成纤维细跑中,并用病毒诱发受体细胞产生集落的能力作为显性的选择标记。几个考贝的大鼠生长激素DNA整合到小鼠的细胞中。这些转化的小鼠细胞表达了大鼠生长激素特异性mRNA,并分泌成熟的大鼠生长激素。在大鼠细胞中,糖皮质激素调控该基因的表达。我们证明,可以通过克隆转移依靠激素调控的基因,从而证明,这种调控似乎是该基因或其RNA产物的一种内在特性。     

基于位置特异性谱和输入加权神经网络的蛋白质亚细胞定位预测

蛋白质必须处于正确的亚细胞位置才能行使其功能。文章利用PSI-BLAST工具搜索蛋白质序列,提取位点特异性谱中的位点特异性得分矩阵作为蛋白质的一类特征,并计算4等分序列的氨基酸含量以及1～7阶二肽含量作为另外两类特征,由这三类特征一共得到蛋白质序列的12个特征向量。通过设计一个简单加权函数对各类特征向量加权处理,作为神经网络预测器的输入,并使用Levenberg-Marquardt算法代替传统的EBP算法来调整网络权值和阈值,大大提高了训练速度。对具有4类亚细胞位置和12类亚细胞位置的两种蛋白质数据集分别进行＂留一法＂测试和5倍交叉验证测试,总体预测精度分别达到88.4%和83.3%。其中,对4类亚细胞位置数据集的预测效果优于普通BP神经网络、隐马尔可夫模型、模糊K邻近等预测方法,对12类亚细胞位置数据集的预测效果优于支持向量机分类方法。最后还对三类特征采取不同加权比例对预测精度的影响进行了讨论,对选择的八种加权比例的预测结果表明,分别给予三类特征合适的权值系数可以进一步提高预测精度。     

一种嵌合型调控元件在肿瘤靶向基因治疗中的应用

肿瘤靶向基因治疗成功的关键是调控治疗基因在肿瘤细胞中特异、高效地表达。首次构建一种嵌合型表达调控元件,旨在转录水平、转录后水平和翻译水平上实现联合调控目的基因在肿瘤细胞中特异性表达。体外实验表明,在前列腺癌细胞系LNCa P中,该调控元件可将报告基因增强型绿色荧光蛋白(EGFP)和荧光素酶(luciferase)的肿瘤表达特异性分别提高420%和480%。体外细胞存活实验表明,运用该元件调控单纯疱疹病毒-1胸腺激酶(HSV-1 TK)的表达能特异性杀伤LNCa P细胞,验证了该元件可成功用于治疗基因的肿瘤靶向表达。     

图神经网络在单细胞测序数据上的研究进展

单细胞测序技术使得科研人员能够以细胞级别的分辨率进行基因表达数据分析，以此发现组织(如肿瘤组织或器官组织)中具有异质性的细胞。这项技术对癌症病理学的研究、生命发育过程的探索等起到了重大推动作用。单细胞测序数据有着样本量大、特征多且稀疏的特点，因此近些年一些研究工作尝试使用图神经网络进行单细胞测序数据的挖掘。这些研究工作一般先根据细胞内的基因表达信息将单细胞测序数据转化为细胞图结构，然后使用图神经网络聚合细胞间邻域信息来进行细胞表示学习，并在细胞聚类任务和细胞类型标注任务上取得了很好的效果。本文旨在介绍图神经网络在单细胞测序数据挖掘上的研究进展，并设计实验展示scGNN、scGCN和scDeepSort三个主流的用于单细胞测序数据的图神经网络模型的性能。最后，结合研究进展与实验分析，本文对图神经网络处理单细胞测序数据这一领域的未来研究方向进行了展望，以促进图神经网络更好地服务于单细胞测序数据的挖掘。     

运动员剧烈运动后血中应激免疫抑制蛋白的产生

我们曾经报道,大鼠或小鼠在束缚应激后血中产生了一种能抑制免疫功能的应激免疫抑制蛋白,（又称Ｎｅｕ－ｒｏｉｍｍｕｎｅｐｒｏｔｅｉｎ,ＮＩＰ,神经免疫蛋白）。本工作证明,运动员在大运动量的训练后血清中也产生一种能抑制淋巴细胞转化的物质,它的生化特性及分子量与前述大鼠和小鼠中的应激免疫抑制蛋白相同。在体外实验中,应激大鼠的血清培养人淋巴结细胞,获得了与大鼠实验相同的结果,即人淋巴结细胞也能产生应激免疫抑制蛋白。同时小鼠束缚应激的血清和大运动量的人类血清可以分别抑制人正常淋巴细胞和正常小鼠由ＣｏｎＡ诱导的淋巴细胞转化,以上结果表明,这种应激免疫抑制蛋白的种属特异性不强。     

基于初级视觉皮层的同步振荡目标检测模型

提出一种基于初级视觉皮层的目标检测模型,该模型只采用方位选择性细胞和皮层内水平连接等Ｖ１基本单元,它以链码表示的目标轮廓作为知识,允许该知识以时间脉冲的形式控制Ｖ１区内神经细胞的动态活动,使与知识轮廓形状相符合的轮廓内的细胞进入同步振荡状态,实现对视野中特定目标轮廓的识别。计算机仿真结果表明,在较高级皮层的“知识”控制之下,初级视觉皮层结构上实现简单的目标检测是可行的。     

基于初级视觉皮层的图像匹配模型

提出一种基于初级视觉皮层的图像匹配模型。该模型只采用方位选择性细胞和皮层内有限范围水平连接等Ｖ１基本单元,它以链码表示的目标轮廓作为知识,允许该知识以时间脉冲的形式控制Ｖ１区内神经细胞的动态活动,使与知识轮廓形状相符合的轮廓内的细胞,逐步进入并维持在兴奋状态,最终实现对视野中特定目标轮廓的提取     

Odor supported place cell model and goal navigation in rodents

Experiments with rodents demonstrate that visual cues play an important role in the control of hippocampal place cells and spatial navigation. Nevertheless, rats may also rely on auditory, olfactory and somatosensory stimuli for orientation. It is also known that rats can track odors or self-generated scent marks to find a food source. Here we model odor supported place cells by using a simple feed-forward network and analyze the impact of olfactory cues on place cell formation and spatial navigation. The obtained place cells are used to solve a goal navigation task by a novel mechanism based on self-marking by odor patches combined with a Q-learning algorithm. We also analyze the impact of place cell remapping on goal directed behavior when switching between two environments. We emphasize the importance of olfactory cues in place cell formation and show that the utility of environmental and self-generated olfactory cues, together with a mixed navigation strategy, improves goal directed navigation.     

Resolution of uncertainty in prefrontal cortex

Making optimal decisions in the face of uncertain or incomplete information arises as a common problem in everyday behavior, but the neural processes underlying this ability remain poorly understood. A typical case is navigation, in which a subject has to search for a known goal from an unknown location. Navigating under uncertain conditions requires making decisions on the basis of the current belief about location and updating that belief based on incoming information. Here, we use functional magnetic resonance imaging during a maze navigation task to study neural activity relating to the resolution of uncertainty as subjects make sequential decisions to reach a goal. We show that distinct regions of prefrontal cortex are engaged in specific computational functions that are well described by a Bayesian model of decision making. This permits efficient goal-oriented navigation and provides new insights into decision making by humans.     

Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity

 A computational model of hippocampal activity during spatial cognition and navigation tasks is presented. The spatial representation in our model of the rat hippocampus is built on-line during exploration via two processing streams. An allothetic vision-based representation is built by unsupervised Hebbian learning extracting spatio-temporal properties of the environment from visual input. An idiothetic representation is learned based on internal movement-related information provided by path integration. On the level of the hippocampus, allothetic and idiothetic representations are integrated to yield a stable representation of the environment by a population of localized overlapping CA3-CA1 place fields. The hippocampal spatial representation is used as a basis for goal-oriented spatial behavior. We focus on the neural pathway connecting the hippocampus to the nucleus accumbens. Place cells drive a population of locomotor action neurons in the nucleus accumbens. Reward-based learning is applied to map place cell activity into action cell activity. The ensemble action cell activity provides navigational maps to support spatial behavior. We present experimental results obtained with a mobile Khepera robot. Received: 02 July 1999 / Accepted in revised form: 20 March 2000     

Spiking Neurons in a Hierarchical Self-Organizing Map Model Can Learn to Develop Spatial and Temporal Properties of Entorhinal Grid Cells and Hippocampal Place Cells

Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous adaptive robots capable of spatial navigation.     

Locating and navigation mechanism based on place-cell and grid-cell models

Extensive experiments on rats have shown that environmental cues play an important role in goal locating and navigation. Major studies about locating and navigation are carried out based only on place cells. Nevertheless, it is known that navigation may also rely on grid cells. Therefore, we model locating and navigation based on both, thus developing a novel grid-cell model, from which firing fields of grid cells can be obtained. We found a continuous-time dynamic system to describe learning and direction selection. In our simulation experiment, according to the results from physiology experiments, we successfully rebuild place fields of place cells and firing fields of grid cells. We analyzed the factors affecting the locating accuracy. Results show that the learning rate, firing threshold and cell number can influence the outcomes from various tasks. We used our system model to perform a goal navigation task and showed that paths that are changed for every run in one experiment converged to a stable one after several runs.     

Path integration — a network model

Path integration is a primary means of navigation for a number of animals. We present a model which performs path integration with a neural network. This model is based on a neural structure called a sinusoidal array, which allows an efficient representation of vector information with neurons. We show that exact path integration can easily be achieved by a neural network. Thus deviations from the direct home trajectory, found previously in experiments with ants, can not be explained by computational limitations of the nervous system. Instead we suggest that the observed deviations are caused by a strategy to simplify landmark navigation.     

Neural basis for spatial memory in animals: what do hippocampal neurons tell us?

Recent studies relying on the recording of neuronal unit activity in freely moving rats show the existence of two populations of neurons signalling the animal's location or head direction: place cells found primarily in the hippocampus and head direction cells found in brain areas anatomically and functionally related to the hippocampus. The properties of these two neuronal populations suggest that their activity strongly depends upon information cues stemming from the spatial environment, and also suggest their involvement in spatial memory. Place cells and head direction cells would jointly participate in a neural network allowing the animal to orient in space and to store spatial locations in memory. This network would also be operating in humans, in particular for encoding specific events in episodic memory.     

Evidence for hippocampal role in place avoidance other than merely memory storage

Spatial navigation is used as a popular animal model of higher cognitive functions in people. The data suggest that the hippocampus is important for both storing spatial memories and for performing spatial computations necessary for navigation. Animals use multiple behavioral strategies to solve spatial tasks often using multiple memory systems. We investigated how inactivation of the rat hippocampus affects performance in a place avoidance task to determine if the role of the hippocampus in this task could be attributed to memory storage/retrieval or to the computations needed for navigation. Injecting tetrodotoxin (TTX) into both hippocampi impaired conditioned place avoidance, but after injecting only one hippocampus, the rats learned the place avoidance as well as without any injections. Retention of the place avoidance learned with one hippocampus was not impaired when the injection was switched to the hippocampus that had not been injected during learning. The result suggests that during learning, the hippocampus did not store the place avoidance memory.     

A Self-Organizing Model of the Visual Development of Hand-Centred Representations

We show how hand-centred visual representations could develop in the primate posterior parietal and premotor cortices during visually guided learning in a self-organizing neural network model. The model incorporates trace learning in the feed-forward synaptic connections between successive neuronal layers. Trace learning encourages neurons to learn to respond to input images that tend to occur close together in time. We assume that sequences of eye movements are performed around individual scenes containing a fixed hand-object configuration. Trace learning will then encourage individual cells to learn to respond to particular hand-object configurations across different retinal locations. The plausibility of this hypothesis is demonstrated in computer simulations.     

Towards an artificial brain

Three components of a brain model operating on neuromolecular computing principles are described. The first component comprises neurons whose input-output behavior is controlled by significant internal dynamics. Models of discrete enzymatic neurons, reaction-diffusion neurons operating on the basis of the cyclic nucleotide cascade, and neurons controlled by cytoskeletal dynamics are described. The second component of the model is an evolutionary learning algorithm which is used to mold the behavior of enzyme-driven neurons or small networks of these neurons for specific function, usually pattern recognition or target seeking tasks. The evolutionary learning algorithm may be interpreted either as representing the mechanism of variation and natural selection acting on a phylogenetic time scale, or as a conceivable ontogenetic adaptation mechanism. The third component of the model is a memory manipulation scheme, called the reference neuron scheme. In principle it is capable of orchestrating a repertoire of enzyme-driven neurons for coherent function. The existing implementations, however, utilize simple neurons without internal dynamics. Spatial navigation and simple game playing (using tic-tac-toe) provide the task environments that have been used to study the properties of the reference neuron model. A memory-based evolutionary learning algorithm has been developed that can assign credit to the individual neurons in a network. It has been run on standard benchmark tasks, and appears to be quite effective both for conventional neural nets and for networks of discrete enzymatic neurons. The models have the character of artificial worlds in that they map the hierarchy of processes in the brain (at the molecular, neuronal, and network levels), provide a task environment, and use this relatively self-contained setup to develop and evaluate learning and adaptation algorithms.