Expression and regulation of caudal in the lower cyclorrhaphan fly Megaselia

The homeobox gene caudal (cad) regulates posterior development in Drosophila. In early embryos, the cad protein (CAD) is expressed in a posterior-to-anterior concentration gradient, which contributes polarity to the developing embryo. The CAD gradient is complementary to and dependent on the anterior pattern organizer Bicoid (BCD), which represses the translation of ubiquitous maternal cad transcripts in the anterior embryo through a direct interaction with the cad 3′ untranslated region (UTR). Here, we show that early embryos of the lower cyclorrhaphan fly Megaselia express the putative cad orthologue Mab-cad throughout the posterior three quarters of the blastoderm but lack maternal transcripts. In transgenic blastoderm embryos of Drosophila, Mab-cad cis-regulatory DNA drives the expression of a reporter gene in a similar pattern, while Mab-cad 3′ UTR fails to mediate translational repression of a ubiquitously transcribed reporter. For another lower cyclorrhaphan fly (Lonchoptera) and two related outgroup taxa of Cyclorrhapha (Empis, Haematopota), we report maternal cad expression in ovarian follicles. Together, our results suggest that BCD is not required for the translational repression of Mab-cad, and that maternal cad expression was lost in the Megaselia lineage

Simultaneous measurement of time-domain fNIRS and physiological signals during a cognitive task

Functional near-infrared spectroscopy (fNIRS) is a commonly used technique to measure the cerebral vascular response related to brain activation. It is known that systemic physiological processes, either independent or correlated with the stimulation task, can influence the optical signal making its interpretation challenging. The aim of the present work is to investigate the impact of task-evoked changes in the systemic physiology on fNIRS measurements for a cognitive paradigm. For this purpose we carried out simultaneous measurements of time-domain fNIRS on the forehead and systemic physiological signals, i.e. mean blood pressure, heart rate, respiration, galvanic skin response, scalp blood flow (flux) and red blood cell (RBC) concentration changes. We performed measurements on 15 healthy volunteers during a semantic continuous performance task (CPT). The optical data was analyzed in terms of depth-selective moments of distributions of times of flight of photons through the tissue. In addition, cerebral activation was localized by a subsequent fMRI experiment on the same subject population using the same task. We observed strong non-cerebral task-evoked changes in concentration changes of oxygenated hemoglobin in the forehead. We investigated the temporal behavior and mutual correlations between hemoglobin changes and the systemic processes. Mean blood pressure (BP), galvanic skin response (GSR) and heart rate exhibited significant changes during the activation period, whereby BP and GSR showed the highest correlation with optical measurements

Categorical Dimensions of Human Odor Descriptor Space Revealed by Non-Negative Matrix Factorization

In contrast to most other sensory modalities, the basic perceptual dimensions of olfaction remain unclear. Here, we use non-negative matrix factorization (NMF) – a dimensionality reduction technique – to uncover structure in a panel of odor profiles, with each odor defined as a point in multi-dimensional descriptor space. The properties of NMF are favorable for the analysis of such lexical and perceptual data, and lead to a high-dimensional account of odor space. We further provide evidence that odor dimensions apply categorically. That is, odor space is not occupied homogenously, but rather in a discrete and intrinsically clustered manner. We discuss the potential implications of these results for the neural coding of odors, as well as for developing classifiers on larger datasets that may be useful for predicting perceptual qualities from chemical structures

Human TRMT2A methylates tRNA and contributes to translation fidelity

5-Methyluridine (m5U) is one of the most abundant RNA modifications found in cytosolic tRNA. tRNA methyltransferase 2 homolog A (hTRMT2A) is the dedicated mammalian enzyme for m5U formation at tRNA position 54. However, its RNA binding specificity and functional role in the cell are not well understood. Here we dissected structural and sequence requirements for binding and methylation of its RNA targets. Specificity of tRNA modification by hTRMT2A is achieved by a combination of modest binding preference and presence of a uridine in position 54 of tRNAs. Mutational analysis together with cross-linking experiments identified a large hTRMT2A–tRNA binding surface. Furthermore, complementing hTRMT2A interactome studies revealed that hTRMT2A interacts with proteins involved in RNA biogenesis. Finally, we addressed the question of the importance of hTRMT2A function by showing that its knockdown reduces translation fidelity. These findings extend the role of hTRMT2A beyond tRNA modification towards a role in translation

Trnp1 organizes diverse nuclear membrane‐less compartments in neural stem cells

TMF1‐regulated nuclear protein 1 (Trnp1) has been shown to exert potent roles in neural development affecting neural stem cell self‐renewal and brain folding, but its molecular function in the nucleus is still unknown. Here, we show that Trnp1 is a low complexity protein with the capacity to phase separate. Trnp1 interacts with factors located in several nuclear membrane‐less organelles, the nucleolus, nuclear speckles, and condensed chromatin. Importantly, Trnp1 co‐regulates the architecture and function of these nuclear compartments in vitro and in the developing brain in vivo. Deletion of a highly conserved region in the N‐terminal intrinsic disordered region abolishes the capacity of Trnp1 to regulate nucleoli and heterochromatin size, proliferation, and M‐phase length; decreases the capacity to phase separate; and abrogates most of Trnp1 protein interactions. Thus, we identified Trnp1 as a novel regulator of several nuclear membrane‐less compartments, a function important to maintain cells in a self‐renewing proliferative state

Can we identify non-stationary dynamics of trial-to-trial variability?"

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings

A Cytoplasmic Complex Mediates Specific mRNA Recognition and Localization in Yeast

The localization of ash mRNA in yeast requires the binding of She2p and the myosin adaptor protein She3p to its localization element, which is highly specific and leads to the assembly of stable transport complexes

Complex Odor from Plants under Attack: Herbivore's Enemies React to the Whole, Not Its Parts

Background: Insect herbivory induces plant odors that attract herbivores ’ natural enemies. Assuming this attraction emerges from individual compounds, genetic control over odor emission of crops may provide a rationale for manipulating the distribution of predators used for pest control. However, studies on odor perception in vertebrates and invertebrates suggest that olfactory information processing of mixtures results in odor percepts that are a synthetic whole and not a set of components that could function as recognizable individual attractants. Here, we ask if predators respond to herbivoreinduced attractants in odor mixtures or to odor mixture as a whole. Methodology/Principal Findings: We studied a system consisting of Lima bean, the herbivorous mite Tetranychus urticae and the predatory mite Phytoseiulus persimilis. We found that four herbivore-induced bean volatiles are not attractive in pure form while a fifth, methyl salicylate (MeSA), is. Several reduced mixtures deficient in one component compared to the full spider-mite induced blend were not attractive despite the presence of MeSA indicating that the predators cannot detect this component in these odor mixtures. A mixture of all five HIPV is most attractive, when offered together with the noninduced odor of Lima bean. Odors that elicit no response in their pure form were essential components of the attractive mixture. Conclusions/Significance: We conclude that the predatory mites perceive odors as a synthetic whole and that th

A novel method for classifying cortical state to identify the accompanying changes in cerebral hemodynamics

Background: Many brain imaging techniques interpret the haemodynamic response as an indirect indicator of underlying neural activity. However, a challenge when interpreting this blood based signal is how changes in brain state may affect both baseline and stimulus evoked haemodynamics. New method: We developed an Automatic Brain State Classifier (ABSC), validated on data from anaesthetised rodents. It uses vectorised information obtained from the windowed spectral frequency power of the Local Field Potential. Current state is then classified by comparing this vectorised information against that calculated from state specific training datasets. Results: The ABSC identified two user defined brain states (synchronised and desynchronised), with high accuracy (~90%). Baseline haemodynamics were found to be significantly different in the two identified states. During state defined periods of elevated baseline haemodynamics we found significant decreases in evoked haemodynamic responses to somatosensory stimuli. Comparison to existing methods: State classification - The ABSC (~90%) demonstrated greater accuracy than clustering (~66%) or 'power threshold' (~64%) methods of comparison.Haemodynamic averaging - Our novel approach of selectively averaging stimulus evoked haemodynamic trials by brain state yields higher quality data than creating a single average from all trials. Conclusions: The ABSC can account for some of the commonly observed trial-to-trial variability in haemodynamic responses which arises from changes in cortical state. This variability might otherwise be incorrectly attributed to alternative interpretations. A greater understanding of the effects of cortical state on haemodynamic changes could be used to inform techniques such as general linear modelling (GLM), commonly used in fMRI