Natural Variation and Neuromechanical Systems

Natural variation plays an important but subtle and often ignored role in neuromechanical systems. This is especially important when designing for living or hybrid systems \ud which involve a biological or self-assembling component. Accounting for natural variation can be accomplished by taking a population phenomics approach to modeling and analyzing such systems. I will advocate the position that noise in neuromechanical systems is partially represented by natural variation inherent in user physiology. Furthermore, this noise can be augmentative in systems that couple physiological systems with technology. There are several tools and approaches that can be borrowed from computational biology to characterize the populations of users as they interact with the technology. In addition to transplanted approaches, the potential of natural variation can be understood as having a range of effects on both the individual's physiology and function of the living/hybrid system over time. Finally, accounting for natural variation can be put to good use in human-machine system design, as three prescriptions for exploiting variation in design are proposed

Variation in the BrHMA3 coding region controls natural variation in cadmium accumulation in Brassica rapa vegetables

Brassica rapa includes several important leafy vegetable crops with the potential for high cadmium (Cd) accumulation, posing a risk to human health. This study aims to understand the genetic basis underlying the variation in Cd accumulation among B. rapa vegetables. Cd uptake and translocation in 64 B. rapa accessions were compared. The role of the heavy metal ATPase gene BrHMA3 in the variation of Cd accumulation was investigated. BrHMA3 encodes a tonoplast-localized Cd transporter. Five full-length and four truncated haplotypes of the BrHMA3 coding sequence were identified, explaining >80% of the variation in the Cd root to shoot translocation among the 64 accessions and in F2 progeny. Truncated BrHMA3 haplotypes had a 2.3 and 9.3 times higher shoot Cd concentration and Cd translocation ratio, respectively, than full-length haplotypes. When expressed in yeast and Arabidopsis thaliana, full-length BrHMA3 showed activity consistent with a Cd transport function, whereas truncated BrHMA3 did not. Variation in the BrHMA3 promoter sequence had little effect on Cd translocation. Variation in the BrHMA3 coding sequence is a key determinant of Cd translocation to and accumulation in the leaves of B. rapa. Strong alleles of BrHMA3 can be used to breed for B. rapa vegetables that are low in Cd in their edible portions

Directional selection effects on patterns of phenotypic (co)variation in wild populations.

Phenotypic (co)variation is a prerequisite for evolutionary change, and understanding how (co)variation evolves is of crucial importance to the biological sciences. Theoretical models predict that under directional selection, phenotypic (co)variation should evolve in step with the underlying adaptive landscape, increasing the degree of correlation among co-selected traits as well as the amount of genetic variance in the direction of selection. Whether either of these outcomes occurs in natural populations is an open question and thus an important gap in evolutionary theory. Here, we documented changes in the phenotypic (co)variation structure in two separate natural populations in each of two chipmunk species (Tamias alpinus and T. speciosus) undergoing directional selection. In populations where selection was strongest (those of T. alpinus), we observed changes, at least for one population, in phenotypic (co)variation that matched theoretical expectations, namely an increase of both phenotypic integration and (co)variance in the direction of selection and a re-alignment of the major axis of variation with the selection gradient

Transcriptome-wide analysis reveals different categories of response to a standardised immune challenge in a wild rodent

Individuals vary in their immune response and, as a result, some are more susceptible to infectious disease than others. Little is known about the nature of this individual variation in natural populations, or which components of immune pathways are most responsible, but defining this underlying landscape of variation is an essential first step to understanding the drivers of this variation and, ultimately, predicting the outcome of infection. We describe transcriptome-wide variation in response to a standardised immune challenge in wild field voles. We find that markers can be categorised into a limited number of types. For the majority of markers, the response of an individual is dependent on its baseline expression level, with significant enrichment in this category for conventional immune pathways. Another, moderately sized, category contains markers for which the responses of different individuals are also variable but independent of their baseline expression levels. This category lacks any enrichment for conventional immune pathways. We further identify markers which display particularly high individual variability in response, and could be used as markers of immune response in larger studies. Our work shows how a standardised challenge performed on a natural population can reveal the patterns of natural variation in immune response

Limits on cosmological variation of quark masses and strong interaction

We discuss limits on variation of $(m_q/\Lambda_{QCD})$. The results are obtained by studying $n-\alpha$-interaction during Big Bang, Oklo natural nuclear reactor data and limits on variation of the proton $g$-factor from quasar absorpion spectra.Comment: 5 pages, RevTe

Sampling-based speech parameter generation using moment-matching networks

This paper presents sampling-based speech parameter generation using moment-matching networks for Deep Neural Network (DNN)-based speech synthesis. Although people never produce exactly the same speech even if we try to express the same linguistic and para-linguistic information, typical statistical speech synthesis produces completely the same speech, i.e., there is no inter-utterance variation in synthetic speech. To give synthetic speech natural inter-utterance variation, this paper builds DNN acoustic models that make it possible to randomly sample speech parameters. The DNNs are trained so that they make the moments of generated speech parameters close to those of natural speech parameters. Since the variation of speech parameters is compressed into a low-dimensional simple prior noise vector, our algorithm has lower computation cost than direct sampling of speech parameters. As the first step towards generating synthetic speech that has natural inter-utterance variation, this paper investigates whether or not the proposed sampling-based generation deteriorates synthetic speech quality. In evaluation, we compare speech quality of conventional maximum likelihood-based generation and proposed sampling-based generation. The result demonstrates the proposed generation causes no degradation in speech quality.Comment: Submitted to INTERSPEECH 201