Responses to threat-related stimuli are influenced by conscious and unconscious processes, but the neural systems underlying these processes and their relationship to anxiety have not been clearly delineated. Using fMRI, we investigated the neural responses associated with the conscious and unconscious (backwardly masked) perception of fearful faces in healthy volunteers who varied in threat sensitivity (Spielberger trait anxiety scale). Unconscious processing modulated activity only in the basolateral subregion of the amygdala, while conscious processing modulated activity only in the dorsal amygdala (containing the central nucleus). Whereas activation of the dorsal amygdala by conscious stimuli was consistent across subjects and independent of trait anxiety, activity in the basolateral amygdala to unconscious stimuli, and subjects’ reaction times, were predicted by individual differences in trait anxiety. These findings provide a biological basis for the unconscious emotional vigilance characteristic of anxiety and a means for investigating the mechanisms and efficacy of treatments for anxiety.

Polymorphisms in the inducible nitric oxide synthase gene (NOS2) promoter have been associated with clinical outcome from malaria. These include a CCTTT repeat (CCTTTn) 2.5 kilobases upstream from the NOS2 transcription start site, and two single nucleotide substitutions: G–>C at position -954 (G-954C), and C–>T at position -1173 (C-1173T). Although hypothesized to influence NO production in vivo, the functional relevance of (CCTTT)n and G-954C is uncertain because disease association studies have yielded inconsistent results. This study found no association between CCTTT repeat number and levels of plasma NO metabolites or peripheral blood mononuclear cell NOS activity in a cohort of asymptomatic malaria-exposed coastal Papua New Guineans 1-60 years old. This suggests that (CCTTT)n does not independently influence NOS2 transcription in vivo. Neither the G-954C nor the C-1173T polymorphisms were identified in this population, indicating the variability and complexity of selection for NOS2 promoter polymorphisms in different malaria-endemic populations.

The obesogenic effect of a high-fat (HF) diet is counterbalanced by stimulation of energy expenditure and lipid oxidation in response to a meal. The aim of this study was to reveal whether muscle nonshivering thermogenesis could be stimulated by a HF diet, especially in obesity-resistant A/J compared with obesity-prone C57BL/6J (B/6J) mice. Experiments were performed on male mice born and maintained at 30 degrees C. Four-week-old mice were randomly weaned onto a low-fat (LF) or HF diet for 2 wk. In the A/J LF mice, cold exposure (4 degrees C) resulted in hypothermia, whereas the A/J HF, B/6J LF, and B/6J HF mice were cold tolerant. Cold sensitivity of the A/J LF mice was associated with a relatively low whole body energy expenditure under resting conditions, which was normalized by the HF diet. In both strains, the HF diet induced uncoupling protein-1-mediated thermogenesis, with a stronger induction in A/J mice. Only in A/J mice: 1) the HF diet augmented activation of whole body lipid oxidation by cold; and 2) at 30 degrees C, oxygen consumption, total content, and phosphorylation of AMP-activated protein kinase (AMPK), and AICAR-stimulated palmitate oxidation in soleus muscle was increased by the HF diet in parallel with significantly increased leptinemia. Gene expression data in soleus muscle of the A/J HF mice indicated a shift from carbohydrate to fatty acid oxidation. Our results suggest a role for muscle nonshivering thermogenesis and lipid oxidation in the obesity-resistant phenotype of A/J mice and indicate that a HF diet could induce thermogenesis in oxidative muscle, possibly via the leptin-AMPK axis.

SUMMARY: INFERNAL builds consensus RNA secondary structure profiles called covariance models (CMs), and uses them to search nucleic acid sequence databases for homologous RNAs, or to create new sequence- and structure-based multiple sequence alignments. AVAILABILITY: Source code, documentation and benchmark downloadable from http://infernal.janelia.org. INFERNAL is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X.

SUMMARY: Infernal builds probabilistic profiles of the sequence and secondary structure of an RNA family called covariance models (CMs) from structurally annotated multiple sequence alignments given as input. Infernal uses CMs to search for new family members in sequence databases and to create potentially large multiple sequence alignments. Version 1.1 of Infernal introduces a new filter pipeline for RNA homology search based on accelerated profile hidden Markov model (HMM) methods and HMM-banded CM alignment methods. This enables \~{}100-fold acceleration over the previous version and \~{}10 000-fold acceleration over exhaustive non-filtered CM searches. AVAILABILITY: Source code, documentation and the benchmark are downloadable from http://infernal.janelia.org. Infernal is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X. Documentation includes a user’s guide with a tutorial, a discussion of file formats and user options and additional details on methods implemented in the software. CONTACT: nawrockie@janelia.hhmi.org.

Many perceptual processes and neural computations, such as speech recognition, motor control and learning, depend on the ability to measure and mark the passage of time. However, the processes that make such temporal judgements possible are unknown. A number of different hypothetical mechanisms have been advanced, all of which depend on the known, temporally predictable evolution of a neural or psychological state, possibly through oscillations or the gradual decay of a memory trace. Alternatively, judgements of elapsed time might be based on observations of temporally structured, but stochastic processes. Such processes need not be specific to the sense of time; typical neural and sensory processes contain at least some statistical structure across a range of time scales. Here, we investigate the statistical properties of an estimator of elapsed time which is based on a simple family of stochastic process.

We describe a class of models that predict how the instantaneous firing rate of a neuron depends on a dynamic stimulus. The models utilize a learnt pointwise nonlinear transform of the stimulus, followed by a linear filter that acts on the sequence of transformed inputs. In one case, the nonlinear transform is the same at all filter lag-times. Thus, this "input nonlinearity" converts the initial numerical representation of stimulus value to a new representation that provides optimal input to the subsequent linear model. We describe algorithms that estimate both the input nonlinearity and the linear weights simultaneously; and present techniques to regularise and quantify uncertainty in the estimates. In a second approach, the model is generalized to allow a different nonlinear transform of the stimulus value at each lag-time. Although more general, this model is algorithmically more straightforward to fit. However, it has many more degrees of freedom than the first approach, thus requiring more data for accurate estimation. We test the feasibility of these methods on synthetic data, and on responses from a neuron in rodent barrel cortex. The models are shown to predict responses to novel data accurately, and to recover several important neuronal response properties.

Simultaneous recordings of the activity of large neural populations are extremely valuable as they can be used to infer the dynamics and interactions of neurons in a local circuit, shedding light on the computations performed. It is now possible to measure the activity of hundreds of neurons using 2-photon calcium imaging. However, many computations are thought to involve circuits consisting of thousands of neurons, such as cortical barrels in rodent somatosensory cortex. Here we contribute a statistical method for stitching" together sequentially imaged sets of neurons into one model by phrasing the problem as fitting a latent dynamical system with missing observations. This method allows us to substantially expand the population-sizes for which population dynamics can be characterized---beyond the number of simultaneously imaged neurons. In particular, we demonstrate using recordings in mouse somatosensory cortex that this method makes it possible to predict noise correlations between non-simultaneously recorded neuron pairs.

Live-cell imaging and particle tracking provide rich information on mechanisms of intracellular transport. However, trajectory analysis procedures to infer complex transport dynamics involving stochastic switching between active transport and diffusive motion are lacking. We applied Bayesian model selection to hidden Markov modeling to infer transient transport states from trajectories of mRNA-protein complexes in live mouse hippocampal neurons and metaphase kinetochores in dividing human cells. The software is available at http://hmm-bayes.org/.