116 research outputs found
Oligopeptides impairing the Myc-Max heterodimerization inhibit lung cancer cell proliferation by reducing Myc transcriptional activity
No full textDeregulated CMYC gene causes cell transformation and is often correlated with tumor progression and a worse clinical outcome of cancer patients. The transcription factor Myc functions by heterodimerizing with its partner, Max. As a strategy to inhibit Myc activity, we have synthesized three small peptides corresponding to segments of the leucine zipper (LZ) region of Max. The purpose of these peptides is to occupy the site of recognition between Myc and Max located in the LZ and inhibit-specific heterodimerization between these proteins. We have used the synthesized oligopeptides in two lung cancer cell lines with different levels of Myc expression. Results demonstrate that: (i) the three peptides resulted equally effective in competing the interaction between Myc and Max in vitro; (ii) they were efficiently internalized into the cells and significantly inhibited cell growth in the cells showing the highest Myc expression; (iii) one specific peptide, only nine aminoacids long, efficiently impaired the transcriptional activity of Myc in vivo, showing a more stable interaction with this protein. Our results are relevant to the development of novel anti-tumoral therapeutic strategies, directed to Myc-overexpressing tumors
Sentence processing: linking language to motor chains
No full textA growing body of evidence in cognitive science and neuroscience is pointing towards the existence of a deep interconnection between cognition, perception and action. According to this embodied perspective language understanding is based on a mental simulation process involving a sensory-motor matching system known as the mirror neuron system. However, the precise dynamics underling the relation between language and action are not yet well understood. In fact, experimental studies are not always coherent as some report that language processing interferes with action execution while others find facilitation. In this work we present a detailed neural network model capable of reproducing experimentally observed influences of the processing of action-related sentences on the execution of motor sequences. The proposed model is based on three main points. The first is that the processing of action-related sentences causes the resonance of motor and mirror neurons encoding the corresponding actions. The second is that there exists a varying degree of crosstalk between neuronal populations depending on whether they encode the same motor act, the same effector or the same action-goal. The third is the fact that neuronal populations’ internal dynamics, which results from the combination of multiple processes taking place at different time scales, can facilitate or interfere with successive activations of the same or of partially overlapping pools
The intentional stance as structure learning: a computational perspective on mindreading
No full textRecent theories of mindreading explain the recognition of action, intention, and belief of other agents in terms of generative architectures that model the causal relations between observables (e.g., observed movements) and their hidden causes (e.g., action goals and beliefs). Two kinds of probabilistic generative schemes have been proposed in cognitive science and robotics that link to a “theory theory” and “simulation theory” of mindreading, respectively. The former compares perceived actions to optimal plans derived from rationality principles and conceptual theories of others’ minds. The latter reuses one’s own internal (inverse and forward) models for action execution to perform a look-ahead mental simulation of perceived actions. Both theories, however, leave one question unanswered: how are the generative models – including task structure and parameters – learned in the first place? We start from Dennett’s “intentional stance” proposal and characterize it within generative theories of action and intention recognition. We propose that humans use an intentional stance as a learning bias that sidesteps the (hard) structure learning problem and bootstraps the acquisition of generative models for others’ actions. The intentional stance corresponds to a candidate structure in the generative scheme, which encodes a simplified belief-desire folk psychology and a hierarchical intention-to-action organization of behavior. This simple structure can be used as a proxy for the “true” generative structure of others’ actions and intentions and is continuously grown and refined – via state and parameter learning – during interactions. In turn – as our computational simulations show – this can help solve mindreading problems and bootstrap the acquisition of useful causal models of both one’s own and others’ goal-directed actions
Neuronal chains for actions in the parietal lobe: a computational model.
No full textThe inferior part of the parietal lobe (IPL) is known to play a very important role in sensorimotor integration. Neurons in this region code goal-related motor acts performed with the mouth, with the hand and with the arm. It has been demonstrated that most IPL motor neurons coding a specific motor act (e.g., grasping) show markedly different activation patterns according to the final goal of the action sequence in which the act is embedded (grasping for eating or grasping for placing). Some of these neurons (parietal mirror neurons) show a similar selectivity also during the observation of the same action sequences when executed by others. Thus, it appears that the neuronal response occurring during the execution and the observation of a specific grasping act codes not only the executed motor act, but also the agent's final goal (intention).In this work we present a biologically inspired neural network architecture that models mechanisms of motor sequences execution and recognition. In this network, pools composed of motor and mirror neurons that encode motor acts of a sequence are arranged in form of action goal-specific neuronal chains. The execution and the recognition of actions is achieved through the propagation of activity bursts along specific chains modulated by visual and somatosensory inputs.The implemented spiking neuron network is able to reproduce the results found in neurophysiological recordings of parietal neurons during task performance and provides a biologically plausible implementation of the action selection and recognition process.Finally, the present paper proposes a mechanism for the formation of new neural chains by linking together in a sequential manner neurons that represent subsequent motor acts, thus producing goal-directed sequences
A Programmer-Interpreter neural network architecture for prefrontal cognitive control
No full textThere is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on behavior by biasing processing toward task-relevant information and by modulating response selection. This idea is typically framed in terms of top-down influences within a cortical control hierarchy, where prefrontal-basal ganglia loops gate multiple input-output channels, which in turn can activate or sequence motor primitives expressed in (pre-)motor cortices. Here we advance a new hypothesis, based on the notion of programmability and an interpreter-programmer computational scheme, on how the PFC can flexibly bias the selection of sensorimotor patterns depending on internal goal and task contexts. In this approach, multiple elementary behaviors representing motor primitives are expressed by a single multi-purpose neural network, which is seen as a reusable area of "recycled" neurons (interpreter). The PFC thus acts as a "programmer" that, without modifying the network connectivity, feeds the interpreter networks with specific input parameters encoding the programs (corresponding to network structures) to be interpreted by the (pre-)motor areas. Our architecture is validated in a standard test for executive function: the 1-2-AX task. Our results show that this computational framework provides a robust, scalable and flexible scheme that can be iterated at different hierarchical layers, supporting the realization of multiple goals. We discuss the plausibility of the "programmer-interpreter" scheme to explain the functioning of prefrontal-(pre)motor cortical hierarchie
Sentence processing: Linking language to motor chain. (Special topic, Action and language integration in cognitive systems.)
No full textA growing body of evidence in cognitive science and neuroscience points towards the existence of a deep interconnection between cognition, perception and action. According to this embodied perspective language is grounded in the sensorimotor system and language understanding is based on a mental simulation process (Jeannerod, 2007; Gallese, 2008; Barsalou, 2009). This means that during action words and sentence comprehension the same perception, action, and emotion mechanisms implied during interaction with objects are recruited. Among the neural underpinnings of this simulation process an important role is played by a sensorimotor matching system known as the mirror neuron system (Rizzolatti and Craighero, 2004). Despite a growing number of studies, the precise dynamics underlying the relation between language and action are not yet well understood. In fact, experimental studies are not always coherent as some report that language processing interferes with action execution while others find facilitation. In this work we present a detailed neural network model capable of reproducing experimentally observed influences of the processing of action-related sentences on the execution of motor sequences. The proposed model is based on three main points. The first is that the processing of action-related sentences causes the resonance of motor and mirror neurons encoding the corresponding actions. The second is that there exists a varying degree of crosstalk between neuronal populations depending on whether they encode the same motor act, the same effector or the same action-goal. The third is the fact that neuronal populations' internal dynamics, which results from the combination of multiple processes taking place at different time scales, can facilitate or interfere with successive activations of the same or of partially overlapping pools
Anchor residue motifs of HLA class I-binding peptides analysed by direct binding of synthetic peptides to HLA class I alpha chains
No full textThe binding characteristics of the primary anchor residue motifs reported for HLA-A2 (A*0201, A*0205) and HLA-B27 (B*2705) alleles were investigated by a direct binding assay of the pertinent synthetic peptides to HLA class I alpha chains derived from a panel of HLA homozygous B-cell lines of various HLA phenotypes, including four A2 subtypes. The assay is based on a serologic detection of the conformational change of HLA class I alpha chains induced by binding to specific peptides in the presence of beta 2m. It is applicable to test a large number of HLA allelic products and synthetic peptides. Assay data confirmed the high allele specificity of the anchor residue motifs tested, but also revealed the intra- and interlocus cross-reactivity of these motifs. In the case of A2 anchor motifs, not only a broad cross-reactivity within the A2 subgroup, but also cross-reactivities with A24, A26, A28, and A29 were observed. With B27 anchor motifs, an interlocus cross-reactivity with A3 and A31 was seen. Several peptides, even though they carried A2 or B27 major anchor residue motifs, failed to bind to the relevant alpha chains, suggesting that the presence of a primary anchor residue motif is necessary for HLA class-I-peptide binding but is not by itself sufficient to guarantee binding
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