Adaptation to stable and unstable dynamics achieved by combined impedance control and inverse dynamics model.

This study compared adaptation in novel force fields where trajectories were initially either stable or unstable to elucidate the processes of learning novel skills and adapting to new environments. Subjects learned to move in a null force field (NF), which was unexpectedly changed either to a velocity-dependent force field (VF), which resulted in perturbed but stable hand trajectories, or a position-dependent divergent force field (DF), which resulted in unstable trajectories. With practice, subjects learned to compensate for the perturbations produced by both force fields. Adaptation was characterized by an initial increase in the activation of all muscles followed by a gradual reduction. The time course of the increase in activation was correlated with a reduction in hand-path error for the DF but not for the VF. Adaptation to the VF could have been achieved solely by formation of an inverse dynamics model and adaptation to the DF solely by impedance control. However, indices of learning, such as hand-path error, joint torque, and electromyographic activation and deactivation suggest that the CNS combined these processes during adaptation to both force fields. Our results suggest that during the early phase of learning there is an increase in endpoint stiffness that serves to reduce hand-path error and provides additional stability, regardless of whether the dynamics are stable or unstable. We suggest that the motor control system utilizes an inverse dynamics model to learn the mean dynamics and an impedance controller to assist in the formation of the inverse dynamics model and to generate needed stability.

Impedance control and internal model use during the initial stage of adaptation to novel dynamics in humans.

This study investigated the neuromuscular mechanisms underlying the initial stage of adaptation to novel dynamics. A destabilizing velocity-dependent force field (VF) was introduced for sets of three consecutive trials. Between sets a random number of 4-8 null field trials were interposed, where the VF was inactivated. This prevented subjects from learning the novel dynamics, making it possible to repeatedly recreate the initial adaptive response. We were able to investigate detailed changes in neural control between the first, second and third VF trials. We identified two feedforward control mechanisms, which were initiated on the second VF trial and resulted in a 50% reduction in the hand path error. Responses to disturbances encountered on the first VF trial were feedback in nature, i.e. reflexes and voluntary correction of errors. However, on the second VF trial, muscle activation patterns were modified in anticipation of the effects of the force field. Feedforward cocontraction of all muscles was used to increase the viscoelastic impedance of the arm. While stiffening the arm, subjects also exerted a lateral force to counteract the perturbing effect of the force field. These anticipatory actions indicate that the central nervous system responds rapidly to counteract hitherto unfamiliar disturbances by a combination of increased viscoelastic impedance and formation of a crude internal dynamics model.

Evolutionary characteristics of exons expressed at different abundance levels in mammals

With comparative genomics approaches, we evaluated the evolutionary characteristics of conservation of exons which are expressed abundantly, moderately or lowly in mammals. Using non-coding regions and pseudogenes as controls, sequence identity, phastCons

Duplication and independent selection of cell-wall invertase genes GIF1 and OsCIN1 during rice evolution and domestication

Background: Various evolutionary models have been proposed to interpret the fate of paralogous duplicates, which provides substrates on which evolution selection could act. In particular, domestication, as a special selection, has played important role in crop cultivation with divergence of many genes controlling important agronomic traits. Recent studies have indicated that a pair of duplicate genes was often sub-functionalized from their ancestral functions held by the parental genes. We previously demonstrated that the rice cell-wall invertase (CWI) gene GIF1 that plays an important role in the grain-filling process was most likely subjected to domestication selection in the promoter region. Here, we report that GIF1 and another CWI gene OsCIN1 constitute a pair of duplicate genes with differentiated expression and function through independent selection. Results: Through synteny analysis, we show that GIF1 and another cell-wall invertase gene OsCIN1 were paralogues derived from a segmental duplication originated during genome duplication of grasses. Results based on analyses of population genetics and gene phylogenetic tree of 25 cultivars and 25 wild rice sequences demonstrated that OsCIN1 was also artificially selected during rice domestication with a fixed mutation in the coding region, in contrast to GIF1 that was selected in the promoter region. GIF1 and OsCIN1 have evolved into different expression patterns and probable different kinetics parameters of enzymatic activity with the latter displaying less enzymatic activity. Overexpression of GIF1 and OsCIN1 also resulted in different phenotypes, suggesting that OsCIN1 might regulate other unrecognized biological process. Conclusion: How gene duplication and divergence contribute to genetic novelty and morphological adaptation has been an interesting issue to geneticists and biologists. Our discovery that the duplicated pair of GIF1 and OsCIN1 has experiencedsub-functionalization implies that selection could act independently on each duplicate towards different functional specificity, which provides a vivid example for evolution of genetic novelties in a model crop. Our results also further support the established hypothesis that gene duplication with sub-functionalization could be one solution for genetic adaptive conflict.

Evolutionary status of Entamoeba

In addition to its medical importance as parasitic pathogen, Entamoeba has aroused people's interest in its evolutionary status for a long time. Lacking mitochondrion and other intracellular organelles common to typical eukaryotes, Entamoeba and several other amitochondrial protozoans have been recognized as ancient pre-mitochondriate eukaryotes and named "archezoa", the most primitive extant eukaryotes. It was suggested that they might be living fossils that remained in a primitive stage of evolution before acquisition of organelles, lying close to the transition between prokaryotes and eukaryotes. However, recent studies revealed that Entamoeba contained an organelle, "crypton" or "mitosome", which was regarded as specialized or reductive mitochondrion. Relative molecular phylogenetic analyses also indicated the existence or the probable existence of mitochondrion in Entamoeba. Our phylogenetic analysis based on DNA topoisomerase II strongly suggested its divergence after some mitchondriate eukaryotes. Here, all these recent researches are reviewed and the evolutionary status of Entamoeba is discussed.

Unsupervised intra-lingual and cross-lingual speaker adaptation for HMM-based speech synthesis using two-pass decision tree construction

Hidden Markov model (HMM)-based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset. Speaker adaptation methods used in the field of HMM-based automatic speech recognition (ASR) are adopted for this task. In the case of unsupervised speaker adaptation, previous work has used a supplementary set of acoustic models to estimate the transcription of the adaptation data. This paper first presents an approach to the unsupervised speaker adaptation task for HMM-based speech synthesis models which avoids the need for such supplementary acoustic models. This is achieved by defining a mapping between HMM-based synthesis models and ASR-style models, via a two-pass decision tree construction process. Second, it is shown that this mapping also enables unsupervised adaptation of HMM-based speech synthesis models without the need to perform linguistic analysis of the estimated transcription of the adaptation data. Third, this paper demonstrates how this technique lends itself to the task of unsupervised cross-lingual adaptation of HMM-based speech synthesis models, and explains the advantages of such an approach. Finally, listener evaluations reveal that the proposed unsupervised adaptation methods deliver performance approaching that of supervised adaptation.

High Altitude Adaptation and Phylogenetic Analysis of Tibetan Horse Based on the Mitochondrial Genome

研究测定了西藏那曲(4,500 m)、云南中甸(3,300 m)、云南德钦(3,300 m)地区3匹藏马线粒体全基因组序列.3个地区的藏马线粒体基因组全长以及结构均与韩国济州岛的马类似,但比瑞典马线粒体基因组短.藏马基因组在DNA序列上的两两相似性达99.3%.通过对线粒体蛋白编码区的分析发现,NADH6基因的蛋白序列在三匹藏马中均表现快速进化的现象.这表明NADH6基因在藏马高原适应进化过程中扮演着重要角色.此外,利用7匹藏马的D-loop区域序列以及与其亲缘关系较近的马的序列首次构建的藏马的系统发育树显示,那曲藏马与中甸、德钦藏马属于不同的分支,且存在较大的遗传多样性,表明藏马可能为多地区起源.

Active dendrites: Adaptation to spike-based communication

Computational analyses of dendritic computations often assume stationary inputs to neurons, ignoring the pulsatile nature of spike-based communication between neurons and the moment-to-moment fluctuations caused by such spiking inputs. Conversely, circuit computations with spiking neurons are usually formalized without regard to the rich nonlinear nature of dendritic processing. Here we address the computational challenge faced by neurons that compute and represent analogue quantities but communicate with digital spikes, and show that reliable computation of even purely linear functions of inputs can require the interplay of strongly nonlinear subunits within the postsynaptic dendritic tree.Our theory predicts a matching of dendritic nonlinearities and synaptic weight distributions to the joint statistics of presynaptic inputs. This approach suggests normative roles for some puzzling forms of nonlinear dendritic dynamics and plasticity.

Improving LVCSR system combination using neural network language model cross adaptation

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple subsystems developed at different sites. Cross system adaptation can be used as an alternative to direct hypothesis level combination schemes such as ROVER. The standard approach involves only cross adapting acoustic models. To fully exploit the complimentary features among sub-systems, language model (LM) cross adaptation techniques can be used. Previous research on multi-level n-gram LM cross adaptation is extended to further include the cross adaptation of neural network LMs in this paper. Using this improved LM cross adaptation framework, significant error rate gains of 4.0%-7.1% relative were obtained over acoustic model only cross adaptation when combining a range of Chinese LVCSR sub-systems used in the 2010 and 2011 DARPA GALE evaluations. Copyright © 2011 ISCA.

Integrated online speaker clustering and adaptation

For many applications, it is necessary to produce speech transcriptions in a causal fashion. To produce high quality transcripts, speaker adaptation is often used. This requires online speaker clustering and incremental adaptation techniques to be developed. This paper presents an integrated approach to online speaker clustering and adaptation which allows efficient clustering of speakers using the same accumulated statistics that are normally used for adaptation. Using a consistent criterion for both clustering and adaptation should yield gains for both stages. The proposed approach is evaluated on a meetings transcription task using audio from multiple distant microphones. Consistent gains over standard clustering and adaptation were obtained. Copyright © 2011 ISCA.

Use of contexts in language model interpolation and adaptation

Language models (LMs) are often constructed by building multiple individual component models that are combined using context independent interpolation weights. By tuning these weights, using either perplexity or discriminative approaches, it is possible to adapt LMs to a particular task. This paper investigates the use of context dependent weighting in both interpolation and test-time adaptation of language models. Depending on the previous word contexts, a discrete history weighting function is used to adjust the contribution from each component model. As this dramatically increases the number of parameters to estimate, robust weight estimation schemes are required. Several approaches are described in this paper. The first approach is based on MAP estimation where interpolation weights of lower order contexts are used as smoothing priors. The second approach uses training data to ensure robust estimation of LM interpolation weights. This can also serve as a smoothing prior for MAP adaptation. A normalized perplexity metric is proposed to handle the bias of the standard perplexity criterion to corpus size. A range of schemes to combine weight information obtained from training data and test data hypotheses are also proposed to improve robustness during context dependent LM adaptation. In addition, a minimum Bayes' risk (MBR) based discriminative training scheme is also proposed. An efficient weighted finite state transducer (WFST) decoding algorithm for context dependent interpolation is also presented. The proposed technique was evaluated using a state-of-the-art Mandarin Chinese broadcast speech transcription task. Character error rate (CER) reductions up to 7.3 relative were obtained as well as consistent perplexity improvements. © 2012 Elsevier Ltd. All rights reserved.