Molecular phylogenetic position of the genera Stephanonympha and Caduceia (Parabasalia) inferred from nuclear small subunit rRNA gene sequences

Nuclear small subunit (SSU) rRNA gene sequences were obtained by polymerase chain reaction from trichomonad symbionts of termites that belong to the Devescovinidae (Caduceia versatilis) and polymastigont Calonymphidae (Stephanonympha nelumbium). The unidentified SSU rRNA sequence Nk3, previously obtained from the termite Neotermes koshunensis, has also been shown to derive from a Stephanonympha sp. by in situ hybridization. These sequences were analysed in a broad phylogeny including nearly all identified parabasalid sequences available in the databases, and some as yet unidentified sequences likely deriving from the new order Cristamonadida (Devescovinidae, Calonymphidae, and hypermastigids Lophomonadida). A global phylogeny of parabasalids reveals a partial agreement between the clades identified in this work and the last classification of this phylum into four orders. However, this classification is still incongruent with our data and new taxonomic considerations are proposed. The analysis confirms the monophyly of the Cristamonadida and separates this order into two groups: the first unites nearly all the Devescovinidae including Caduceia and the Calonymphidae Coronympha and Metacoronympha, whereas the second group is composed of a few Devescovinidae, Lophomonadida, and Calonymphidae such as Stephanonympha. Caduceia is closely related to Devescovina, corroborating the marked morphological similarity between these two genera whereas Stephanonympha groups together with the Calonymphidae Snyderella and Calonympha. These data also confirm the polyphyly of the families Devescovinidae and Calonymphidae and support the arrangement of the axostyle-pelta complexes as a valuable character for taxonomic considerations within the Calonymphidae.     

Hedging your bets by learning reward correlations in the human brain

Human subjects are proficient at tracking the mean and variance of rewards and updating these via prediction errors. Here, we addressed whether humans can also learn about higher-order relationships between distinct environmental outcomes, a defining ecological feature of contexts where multiple sources of rewards are available. By manipulating the degree to which distinct outcomes are correlated, we show that subjects implemented an explicit model-based strategy to learn the associated outcome correlations and were adept in using that information to dynamically adjust their choices in a task that required a minimization of outcome variance. Importantly, the experimentally generated outcome correlations were explicitly represented neuronally in right midinsula with a learning prediction error signal expressed in rostral anterior cingulate cortex. Thus, our data show that the human brain represents higher-order correlation structures between rewards, a core adaptive ability whose immediate benefit is optimized sampling.     

Disentangling the roles of approach, activation and valence in instrumental and pavlovian responding

Hard-wired, Pavlovian, responses elicited by predictions of rewards and punishments exert significant benevolent and malevolent influences over instrumentally-appropriate actions. These influences come in two main groups, defined along anatomical, pharmacological, behavioural and functional lines. Investigations of the influences have so far concentrated on the groups as a whole; here we take the critical step of looking inside each group, using a detailed reinforcement learning model to distinguish effects to do with value, specific actions, and general activation or inhibition. We show a high degree of sophistication in Pavlovian influences, with appetitive Pavlovian stimuli specifically promoting approach and inhibiting withdrawal, and aversive Pavlovian stimuli promoting withdrawal and inhibiting approach. These influences account for differences in the instrumental performance of approach and withdrawal behaviours. Finally, although losses are as informative as gains, we find that subjects neglect losses in their instrumental learning. Our findings argue for a view of the Pavlovian system as a constraint or prior, facilitating learning by alleviating computational costs that come with increased flexibility.     

ICAT-MS-MS time course analysis of atrophying mouse skeletal muscle cytosolic subproteome

Skeletal muscle atrophy is a process in which protein degradation exceeds protein synthesis, resulting in a decrease of the muscle's physiological cross-sectional area and mass, and is often a serious consequence of numerous health problems. We used the isotope-coded affinity tag (ICAT) labelling approach and MS-MS to protein profile cytosolic subcellular fractions from mouse tibialis anterior skeletal muscle undergoing 0, 4, 8, or 16 days of immobilisation-induced atrophy. For the validation of peptide and protein identifications statistical algorithms were applied to the sequence database search results in order to obtain consistent sensitivity/error rates for protein and peptide identifications at each immobilisation time point. In this study, we identified and quantified a large number of mouse skeletal muscle proteins. At a protein probability (P) of P> or = 0.9 (corresponding to a false positive error rate of less than 1%) 807 proteins were identified (231, 226, 217 for 4, 8, 16 days of immobilisation and 133 for the control sample, respectively), from which 51 displayed altered protein abundance with atrophy. Due to randomness of data acquisition, a full time course could be generated only for 62 proteins, most of which displayed unchanged protein abundance. In spite of this, useful information about dataset characteristics and underlying biological processes could be obtained through gene over-representation analysis. 20 gene categories-mainly but not exclusively encoded by the subset of overlapping proteins--were consistently found to be significantly (p < 0.05) over-represented in all 4 sub-datasets.     

Spine biomechanics

Current trends in spine research are reviewed in order to suggest future opportunities for biomechanics. Recent studies show that psychosocial factors influence back pain behaviour but are not important causes of pain itself. Severe back pain most often arises from intervertebral discs, apophyseal joints and sacroiliac joints, and physical disruption of these structures is strongly but variably linked to pain. Typical forms of structural disruption can be reproduced by severe mechanical loading in-vitro, with genetic and age-related weakening sometimes leading to injury under moderate loading. Biomechanics can be used to quantify spinal loading and movements, to analyse load distributions and injury mechanisms, and to develop therapeutic interventions. The authors suggest that techniques for quantifying spinal loading should be capable of measurement "in the field" so that they can be used in epidemiological surveys and ergonomic interventions. Great accuracy is not required for this task, because injury risk depends on tissue weakness as much as peak loading. Biomechanical tissue testing and finite-element modelling should complement each other, with experiments establishing proof of concept, and models supplying detail and optimising designs. Suggested priority areas for future research include: understanding interactions between intervertebral discs and adjacent vertebrae; developing prosthetic and tissue-engineered discs; and quantifying spinal function during rehabilitation. "Mechanobiology" has perhaps the greatest future potential, because spinal degeneration and healing are both mediated by the activity of cells which are acutely sensitive to their local mechanical environment. Precise characterisation and manipulation of this environment will be a major challenge for spine biomechanics.