造血干细胞移植资格准入的相关问题

通过探讨目前我国开展造血干细胞移植(HSCT)存在的问题,介绍开展HSCT的基本要求,引入准入制度的重要意义来保护这项医疗技术不被滥用,使其最终的结果是HSCT的技术得到稳步提高,移植质量得到有效保障。     

持续性植物状态若干问题的探讨

明确意识的含义及意识障碍的分类是分析持续性植物状态的前提。根据多年临床经验对持续性植物状态的定义、评分标准、诊断与鉴别诊断进行深入割析。植物状态的研究取得相当进展,但从诊断、评分到治疗手段仍面临诸多困惑,而神经干细胞移植和神经电刺激术是未来希望所在。     

用比较治疗学的观点谈早期2型糖尿病的胰岛素强化治疗

现在越来越多的证据显示短期胰岛素强化治疗可以使血糖接近正常,并明显改善胰岛β细胞功能,但是也有专家认为这种胰岛素治疗的代价比能带来的好处更多。本文运用比较治疗学的观点就这一问题进行了探讨,并强调了胰岛素强化治疗在早期2型糖尿病治疗中的重要意义。     

A neural model of how the brain computes heading from optic flow in realistic scenes

Visually-based navigation is a key competence during spatial cognition. Animals avoid obstacles and approach goals in novel cluttered environments using optic flow to compute heading with respect to the environment. Most navigation models try either explain data, or to demonstrate navigational competence in real-world environments without regard to behavioral and neural substrates. The current article develops a model that does both. The ViSTARS neural model describes interactions among neurons in the primate magnocellular pathway, including V1, MT⁺, and MSTd. Model outputs are quantitatively similar to human heading data in response to complex natural scenes. The model estimates heading to within 1.5° in random dot or photo-realistically rendered scenes, and within 3° in video streams from driving in real-world environments. Simulated rotations of less than 1°/s do not affect heading estimates, but faster simulated rotation rates do, as in humans. The model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.     

Perception with compensatory devices: from sensory substitution to sensorimotor extension

Sensory substitution devices provide through an unusual sensory modality (the substituting modality, e.g., audition) access to features of the world that are normally accessed through another sensory modality (the substituted modality, e.g., vision). In this article, we address the question of which sensory modality the acquired perception belongs to. We have recourse to the four traditional criteria that have been used to define sensory modalities: sensory organ, stimuli, properties, and qualitative experience ( Grice, 1962 ), to which we have added the criteria of behavioral equivalence ( Morgan, 1977 ), dedication ( Keeley, 2002 ), and sensorimotor equivalence ( O'Regan & Noë, 2001 ). We discuss which of them are fulfilled by perception through sensory substitution devices and whether this favors the view that perception belongs to the substituting or to the substituted modality. Though the application of a number of criteria might be taken to point to the conclusion that perception with a sensory substitution device belongs to the substituted modality, we argue that the evidence leads to an alternative view on sensory substitution. According to this view, the experience after sensory substitution is a transformation, extension, or augmentation of our perceptual capacities, rather than being something equivalent or reducible to an already existing sensory modality. We develop this view by comparing sensory substitution devices to other "mind-enhancing tools" such as pen and paper, sketchpads, or calculators. An analysis of sensory substitution in terms of mind-enhancing tools unveils it as a thoroughly transforming perceptual experience and as giving rise to a novel form of perceptual interaction with the environment.     

干细胞移植治疗冠心病的现状与未来

冠心病仍然是全球的主要死亡原因.其所造成的心肌梗死及慢性心力衰竭对生活质量的影响和由此造成的高额医疗费用促使我们不断寻求新的医疗措施.近几年有关干细胞用于心肌梗死后心肌再生、心肌内新生血管形成,可能还会改善梗死后的心功能的研究成为心脏病学研究的最热门领域.尽管干细胞治疗有可能使心力衰竭的治疗得到彻底的革命,但仍有部分问题需要澄清.已发表的各个研究结果设计不尽相同,致使对该项治疗的效果较难得出一致的结论.但这些研究都强调干细胞治疗在未来心血管疾病的治疗中可能会发挥较大的作用.干细胞治疗与药学、外科及介入治疗的结合会显著改善冠心病患者的预后.     

Identification and integration of sensory modalities: Neural basis and relation to consciousness

A key question in studying consciousness is how neural operations in the brain can identify streams of sensory input as belonging to distinct modalities, which contributes to the representation of qualitatively different experiences. The basis for identification of modalities is proposed to be constituted by self-organized comparative operations across a network of unimodal and multimodal sensory areas. However, such network interactions alone cannot answer the question how sensory feature detectors collectively account for an integrated, yet phenomenally differentiated experiential content. This problem turns out to be different from, although related to, the binding problem. It is proposed that the neural correlate of an enriched, multimodal experience is constituted by the attractor state of a dynamic associative network. Within this network, unimodal and multimodal sensory maps continuously interact to influence each other’s attractor state, so that a feature change in one modality results in a fast re-coding of feature information in another modality. In this scheme, feature detection is coded by firing-rate, whereas firing phase codes relational aspects.     

Abductive reasoning in neural-symbolic systems

Abduction is or subsumes a process of inference. It entertains possible hypotheses and it chooses hypotheses for further scrutiny. There is a large literature on various aspects of non-symbolic, subconscious abduction. There is also a very active research community working on the symbolic (logical) characterisation of abduction, which typically treats it as a form of hypothetico-deductive reasoning. In this paper we start to bridge the gap between the symbolic and sub-symbolic approaches to abduction. We are interested in benefiting from developments made by each community. In particular, we are interested in the ability of non-symbolic systems (neural networks) to learn from experience using efficient algorithms and to perform massively parallel computations of alternative abductive explanations. At the same time, we would like to benefit from the rigour and semantic clarity of symbolic logic. We present two approaches to dealing with abduction in neural networks. One of them uses Connectionist Modal Logic and a translation of Horn clauses into modal clauses to come up with a neural network ensemble that computes abductive explanations in a top-down fashion. The other combines neural-symbolic systems and abductive logic programming and proposes a neural architecture which performs a more systematic, bottom-up computation of alternative abductive explanations. Both approaches employ standard neural network architectures which are already known to be highly effective in practical learning applications. Differently from previous work in the area, our aim is to promote the integration of reasoning and learning in a way that the neural network provides the machinery for cognitive computation, inductive learning and hypothetical reasoning, while logic provides the rigour and explanation capability to the systems, facilitating the interaction with the outside world. Although it is left as future work to determine whether the structure of one of the proposed approaches is more amenable to learning than the other, we hope to have contributed to the development of the area by approaching it from the perspective of symbolic and sub-symbolic integration.