Toll-like receptors and innate immune responses in systemic lupus erythematosus

A series of discoveries over the past several years has provided a new paradigm for understanding autoimmunity in systemic lupus erythematosus. The discoveries of pattern recognition receptors and of how these receptors can be recruited into autoimmune responses underpin this paradigm. The implications of these observations continue to unfold with ongoing investigation into the range and specificity of pattern recognition receptors, into how immune complexes containing nucleic acids trigger these receptors, into how endogenous macromolecular 'danger signals' stimulate innate immune responses, and into the effect of pattern recognition receptor activation on various cell types in initiating and perpetuating autoimmunity. The development of clinical trials using therapeutic agents that target components of the innate immune system suggests that these advances may soon culminate in new medications for treating patients with systemic lupus erythematosus.     

Development of rhythmic pattern generators

In contrast to the wealth of knowledge about the organizational rules of adult central pattern generators, far less is known about how these networks are assembled during development. The basic architecture for adult central pattern generators appears early in development but different generators may follow completely different developmental pathways to reach maturity. Recent evidence suggests that neuromodulatory inputs, in addition to their short-term adaptive control of central pattern generator activity, play a crucial role in both the final developmental tuning and the long-term maintenance of adult network function.     

Ethical Know-how and Traditional Ecological Knowledge in Small Scale Fisheries on the Eastern Black Sea Coast of Turkey

In the context of development and changes in two small-boat fisheries on the eastern Black Sea coast of Turkey, this article discusses how institutions are embedded in tradition, and what is “traditional” in “traditional knowledge”. Taking new institutional economics’ focus on institutions and approaches to traditional ecological knowledge as vantage points for discussion I compare systems of informal management in one discontinued and one new fishery to address questions such as: What kinds of knowledges and rules are involved in these kinds of fishing? What constitutes a tradition of knowledge? What is the role of tradition in working out new rules? I couple theories of situated knowledge (Ingold) and embeddedness of rules (Varela), both of which are inspired by Merleau-Ponty’s phenomenological approach, to show how institutions and traditional ecological knowledge are embedded and come together in practice.     

Computational mate choice: theory and empirical evidence

The present review is based on the thesis that mate choice results from information-processing mechanisms governed by computational rules and that, to understand how females choose their mates, we should identify which are the sources of information and how they are used to make decisions. We describe mate choice as a three-step computational process and for each step we present theories and review empirical evidence. The first step is a perceptual process. It describes the acquisition of evidence, that is, how females use multiple cues and signals to assign an attractiveness value to prospective mates (the preference function hypothesis). The second step is a decisional process. It describes the construction of the decision variable (DV), which integrates evidence (private information by direct assessment), priors (public information), and value (perceived utility) of prospective mates into a quantity that is used by a decision rule (DR) to produce a choice. We make the assumption that females are optimal Bayesian decision makers and we derive a formal model of DV that can explain the effects of preference functions, mate copying, social context, and females' state and condition on the patterns of mate choice. The third step of mating decision is a deliberative process that depends on the DRs. We identify two main categories of DRs (absolute and comparative rules), and review the normative models of mate sampling tactics associated to them. We highlight the limits of the normative approach and present a class of computational models (sequential-sampling models) that are based on the assumption that DVs accumulate noisy evidence over time until a decision threshold is reached. These models force us to rethink the dichotomy between comparative and absolute decision rules, between discrimination and recognition, and even between rational and irrational choice. Since they have a robust biological basis, we think they may represent a useful theoretical tool for behavioural ecologist interested in integrating proximate and ultimate causes of mate choice.     

Synthetic lipid-anchored receptors based on the binding site of a monoclonal antibody.

Highly specific ligand receptor interactions generally characterize molecular recognition at cell surfaces and other biological systems. In this study we simulate a membrane receptor by fusing a monoclonal antibody fragment to a phospholipid. A sulfhydryl group in the hinge region of a monoclonal antibody fragment, was covalently linked to derivatives of phosphatidylethanolamines and phosphatidylserine via three different hydrophilic spacer arms. We investigated and characterized these lipid-anchored Fab-fragments which we have named 'Fab-lipids' in liposomal and monolayer systems. Methods for the monomolecular assembling of such films at the air/water interface and techniques used for their manipulation are outlined. We describe two possibilities for building a monomolecular receptor layer, consisting of two-dimensional pattern of oriented Fab-fragments with their artificial hydrophobic anchor embedded in a lipid matrix. In the first method a monomolecular film at the air/water interface was allowed to form from a vesicular suspension and driven into a phase separation, resulting in protein rich domains embedded in a protein depleted phase. This film was transferred onto a solid support in such a way that the established pattern was preserved. Alternatively, a recognition pattern was formed by directly cross-linking the Fab-fragments to preformed planar membranes composed of the reactive spacer-lipids and an inert matrix lipid. Specificity as well as contrast of the binding activity of the receptor layers were qualified using micro-fluorimetry.     

A structure-based model of RIG-I activation

A series of high-resolution crystal structures of RIG-I and RIG-I:dsRNA cocrystals has recently been reported. Comparison of these structures provides considerable insight into how this innate immune pattern recognition receptor is activated upon detecting and binding a certain class of viral RNAs.     

An artificial neural network that utilizes hip joint actuations to control bifurcations and chaos in a passive dynamic bipedal walking model

Chaos is a central feature of human locomotion and has been suggested to be a window to the control mechanisms of locomotion. In this investigation, we explored how the principles of chaos can be used to control locomotion with a passive dynamic bipedal walking model that has a chaotic gait pattern. Our control scheme was based on the scientific evidence that slight perturbations to the unstable manifolds of points in a chaotic system will promote the transition to new stable behaviors embedded in the rich chaotic attractor. Here we demonstrate that hip joint actuations during the swing phase can provide such perturbations for the control of bifurcations and chaos in a locomotive pattern. Our simulations indicated that systematic alterations of the hip joint actuations resulted in rapid transitions to any stable locomotive pattern available in the chaotic locomotive attractor. Based on these insights, we further explored the benefits of having a chaotic gait with a biologically inspired artificial neural network (ANN) that employed this chaotic control scheme. Remarkably, the ANN was quite robust and capable of selecting a hip joint actuation that rapidly transitioned the passive dynamic bipedal model to a stable gait embedded in the chaotic attractor. Additionally, the ANN was capable of using hip joint actuations to accommodate unstable environments and to overcome unforeseen perturbations. Our simulations provide insight on the advantage of having a chaotic locomotive system and provide evidence as to how chaos can be used as an advantageous control scheme for the nervous system.     

Innate instruction of CD4+ T cell immunity in respiratory bacterial infection

The innate immune system recognizes invading microbes via conserved pattern recognition receptors and uses inflammatory signals to concert adaptive defense mechanisms. However, microbial and host parameters involved in CD4 T cell priming and direction of Th1, Th2, and Th17 differentiation in the context of infections with complex pathogens in vivo are incompletely understood. In this study, we used Legionella pneumophila, which triggers membrane-bound and cytosolic pattern recognition receptors, to study the innate instruction of adaptive immunity. Upon airway infection, T cells were primed exclusively in the lung-draining lymph nodes and differentiated into Th1/Th17 effector cells upon arrival in the lung. Although engagement of membrane-bound pattern recognition receptors was sufficient for initial T cell activation and proliferation, cytosolic pattern recognition was required for effector T cell differentiation. In the absence of cytoplasmic pattern recognition, MyD88 was key for T cell priming, whereas, in its presence, MyD88-mediated signals were crucial for Th17 differentiation. Specifically, cytosolic sensing of Legionella-derived flagellin, inflammasome activation, and IL-1 signaling contributed to Th17 development. In the absence of TLR signaling, a simultaneous Th1/Th2 response developed that was independent of the inflammasome-IL-1 axis. Collectively, these data illustrate the important role for various pattern recognition receptors triggered by complex pathogens and how they each instruct specific differentiation programs in responding CD4 T cells.     

Characterizing gene movements between chromosomes in Drosophila

Drosophila have a variety of innate immune strategies for defending itself from infection, including humoral and cell mediated responses to invading microorganisms. At the front lines of these responses, are a diverse group of pattern recognition receptors that recognize pathogen associated molecular patterns. These patterns include bacterial lipopolysaccharides, peptidoglycans, and fungal β?1,3 glucans. Some of the receptors catalytically modify the pathogenic determinant, but all are responsible for directly facilitating a signaling event that results in an immune response. Some of these events require multiple pattern recognition receptors acting sequentially to activate a pathway. In some cases, a signaling pathway may be activated by a variety of different pathogens, through parallel receptors detecting different pathogenic determinants. In this chapter, we review what is known about pattern recognition receptors in Drosophila, and how those lessons may be applied towards a broader understanding of immunity.     

基于表面肌电信号的人体动作识别算法研究进展

表面肌电信号(Surface Electromyography,sEMG)是通过相应肌群表面的传感器记录下来的一维时间序列非平稳生物电信号,不但反映了神经肌肉系统活动,对于反映相应动作肢体活动信息同样重要。而模式识别是肌电应用领域的基础和关键。为了在应用基于表面肌电信号模式识别中选取合适算法,本文拟对基于表面肌电信号的人体动作识别算法进行回顾分析,主要包括模糊模式识别算法、线性判别分析算法、人工神经网络算法和支持向量机算法。模糊模式识别能自适应提取模糊规则,对初始化规则不敏感,适合处理s EMG这样具有严格不重复的生物电信号;线性判别分析对数据进行降维,计算简单,但不适合大数据;人工神经网络可以同时描述训练样本输入输出的线性关系和非线性映射关系,可以解决复杂的分类问题,学习能力强;支持向量机处理小样本、非线性的高维数据优势明显,计算速度快。比较各方法的优缺点,为今后处理此类问题模式识别算法选取提供了参考和依据。     

听觉中枢神经元对声信号的识别和处理

人类听觉的基本特性和机制与其他哺乳动物相似,因此,利用动物所作的听觉研究和获得的结果,有助于认识人类自身的听觉.围绕听觉中枢神经元对不同模式的声信号的识别和处理,简要综述了这方面的研究.声信号和声模式识别在听觉中枢对声信号的感受和加工中具有重要意义.听神经元作为声模式识别的结构和功能基础,对不同的声刺激模式产生不同反应,甚至是在同一声刺激模式下,改变其中的某个声参数,神经元的反应也会发生相应改变,而其反应的特性和机制均需要更多研究来解答.另外,声信号作为声信息的载体,不同的声信息寓于不同的声参数和声特征之中,研究发现,听觉中枢神经元存在相应的声信息甄别和选择的神经基础,能对动态变化的声频率、幅度和时程等进行反应和编码,并且,在不同种类动物上获得的研究结果极为相似,表明听觉中枢对不同声信号和声刺激模式的识别、分析和加工,具有共同性和普遍性.     

Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity

Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli.     

Recurrent violations of invariant rules for offspring size: evidence from turtles and the implications for small clutch size models

Smith and Fretwell’s classic model predicts that parents can maximize fitness by dividing the energy available for reproduction into offspring of an optimal size. However, this model breaks down when clutch size is small (~1–10 offspring). Invariant rules are an extension of the Smith–Fretwell model, and these rules predict how offspring size will vary among and within individuals that produce small clutch sizes. Here, we provide a narrow test of invariant rules using three turtle species, then we synthesize and re-analyze existing data from 18 different species (comprising five Orders) to evaluate whether invariant rules are followed across broad taxa. We do not find support for most invariant rules in turtles, and our re-analysis demonstrates a general mismatch between observed and expected values across all taxa evaluated, suggesting that invariant rules fail to predict reproductive patterns in nature. Morphological constraints on offspring size and reproductive effort may be important reasons for disparities between theory and observation both in turtles and other taxa. Paradoxically, morphological constraints are most common in small-bodied species and individuals, but these same candidates are also those which produce the small clutch sizes that are necessary to test invariant rules, such that a fair test of invariant rules will often be challenging. Mismatches between theory and observation might also occur because theory assumes that mothers exert control over resource allocation to offspring. In fact, there is evidence of widespread genetic correlations among investment per offspring and reproductive effort, such that these traits are not independent.     

Multimodal sensory integration in insects--towards insect brain control architectures

Although a variety of basic insect behaviours have inspired successful robot implementations, more complex capabilities in these 'simple' animals are often overlooked. By reviewing the general architecture of their nervous systems, we gain insight into how they are able to integrate behaviours, perform pattern recognition, context-dependent learning, and combine many sensory inputs in tasks such as navigation. We review in particular what is known about two specific 'higher' areas in the insect brain, the mushroom bodies and the central complex, and how they are involved in controlling an insect's behaviour. While much of the functional interpretation of this information is still speculative, it nevertheless suggests some promising new approaches to obtaining adaptive behaviour in robots.     

A hierarchical neuronal model for generation and online recognition of birdsongs

The neuronal system underlying learning, generation and recognition of song in birds is one of the best-studied systems in the neurosciences. Here, we use these experimental findings to derive a neurobiologically plausible, dynamic, hierarchical model of birdsong generation and transform it into a functional model of birdsong recognition. The generation model consists of neuronal rate models and includes critical anatomical components like the premotor song-control nucleus HVC (proper name), the premotor nucleus RA (robust nucleus of the arcopallium), and a model of the syringeal and respiratory organs. We use Bayesian inference of this dynamical system to derive a possible mechanism for how birds can efficiently and robustly recognize the songs of their conspecifics in an online fashion. Our results indicate that the specific way birdsong is generated enables a listening bird to robustly and rapidly perceive embedded information at multiple time scales of a song. The resulting mechanism can be useful for investigating the functional roles of auditory recognition areas and providing predictions for future birdsong experiments.     

Apoptotic cells activate NKT cells through T cell Ig-like mucin-like-1 resulting in airway hyperreactivity

T cell Ig-like mucin-like-1 (TIM-1) is an important asthma susceptibility gene, but the immunological mechanisms by which TIM-1 functions remain uncertain. TIM-1 is also a receptor for phosphatidylserine (PtdSer), an important marker of cells undergoing programmed cell death, or apoptosis. We now demonstrate that NKT cells constitutively express TIM-1 and become activated by apoptotic cells expressing PtdSer. TIM-1 recognition of PtdSer induced NKT cell activation, proliferation, and cytokine production. Moreover, the induction of apoptosis in airway epithelial cells activated pulmonary NKT cells and unexpectedly resulted in airway hyperreactivity, a cardinal feature of asthma, in an NKT cell-dependent and TIM-1-dependent fashion. These results suggest that TIM-1 serves as a pattern recognition receptor on NKT cells that senses PtdSer on apoptotic cells as a damage-associated molecular pattern. Furthermore, these results provide evidence for a novel innate pathway that results in airway hyperreactivity and may help to explain how TIM-1 and NKT cells regulate asthma.     

Banknote recognition: investigating processing and cognition framework using competitive neural network

Humans are apt at recognizing patterns and discovering even abstract features which are sometimes embedded therein. Our ability to use the banknotes in circulation for business transactions lies in the effortlessness with which we can recognize the different banknote denominations after seeing them over a period of time. More significant is that we can usually recognize these banknote denominations irrespective of what parts of the banknotes are exposed to us visually. Furthermore, our recognition ability is largely unaffected even when these banknotes are partially occluded. In a similar analogy, the robustness of intelligent systems to perform the task of banknote recognition should not collapse under some minimum level of partial occlusion. Artificial neural networks are intelligent systems which from inception have taken many important cues related to structure and learning rules from the human nervous/cognition processing system. Likewise, it has been shown that advances in artificial neural network simulations can help us understand the human nervous/cognition system even furthermore. In this paper, we investigate three cognition hypothetical frameworks to vision-based recognition of banknote denominations using competitive neural networks. In order to make the task more challenging and stress-test the investigated hypotheses, we also consider the recognition of occluded banknotes. The implemented hypothetical systems are tasked to perform fast recognition of banknotes with up to 75 % occlusion. The investigated hypothetical systems are trained on Nigeria’s Naira banknotes and several experiments are performed to demonstrate the findings presented within this work.     

Pattern recognition and associative memory as dynamical processes in a synergetic system

We consider a model for associative memory and pattern recognition which was devised by Haken (1987b). This model treats the activity of the neurons as continuous variables and exploits an analogy with pattern formation in synergetic systems. The capability of such a system to act as associative memory is demonstrated by the reconstruction of faces which are partially offered to the system, and which are restored by the corresponding dynamical process. We demonstrate how this model can be cast into a form which is translation invariant and how partially hidden faces in scenes can be recognized by means of the control of attention parameters of specific patterns.