Oncopeltus fasciatus zen is essential for serosal tissue function in katatrepsis

AbstractUnlike most Hox cluster genes, with their canonical role in anterior–posterior patterning of the embryo, the Hox3 orthologue of insects has diverged. Here, we investigate the zen orthologue in Oncopeltus fasciatus (Hemiptera:Heteroptera). As in other insects, the Of-zen gene is expressed extraembryonically, and RNA interference (RNAi) experiments demonstrate that it is functionally required in this domain for the proper occurrence of katatrepsis, the phase of embryonic movements by which the embryo emerges from the yolk and adjusts its orientation within the egg. After RNAi knockdown of Of-zen, katatrepsis does not occur, causing embryos to complete development inside out. However, not all aspects of expression and function are conserved compared to grasshopper, beetle, and fly orthologues. Of-zen is not expressed in the extraembryonic tissue until relatively late, suggesting it is not involved in tissue specification. Within the extraembryonic domain, Of-zen is expressed in the outer serosal membrane, but unlike orthologues, it is not detectable in the inner extraembryonic membrane, the amnion. Thus, the role of zen in the interaction of serosa, amnion, and embryo may differ between species. Of-zen is also expressed in the blastoderm, although this early expression shows no apparent correlation with defects seen by RNAi knockdown

Antiplasmodial Activities of Some Products from Turreanthus Africanus (Meliaceae)

We investigated the antiplasmodial activity of some pure compounds of Turreanthus africanus (Meliaceae), a plant that is used in traditional medicine to treat malaria in Southwest Cameroon. A phytochemical analysis of the methylene chloride: methanol (1:1) extract of the seeds of the plant yielded seven compounds. Four of them, which were oils, were subjected to in vitro bioassays on Plasmodium falciparum F 32, chloroquine sensitive strain. Compound 1 (16-oxolabda-8 (17), 12(E)-dien-15-oic acid), showed the highest antiplasmodial activity, two others (methyl-14,15-epoxylabda-8 (17), 12(E)-diene-16-oate, and turreanin A), had moderate activity and one was inactive. These findings are consistent with the use of T. africanus in the traditional treatment of P. falciparum malaria

The Anterior Cingulate Cortex Predicts Future States to Mediate Model-Based Action Selection

Behavioral control is not unitary. It comprises parallel systems, model based and model free, that respectively generate flexible and habitual behaviors. Model-based decisions use predictions of the specific consequences of actions, but how these are implemented in the brain is poorly understood. We used calcium imaging and optogenetics in a sequential decision task for mice to show that the anterior cingulate cortex (ACC) predicts the state that actions will lead to, not simply whether they are good or bad, and monitors whether outcomes match these predictions. ACC represents the complete state space of the task, with reward signals that depend strongly on the state where reward is obtained but minimally on the preceding choice. Accordingly, ACC is necessary only for updating model-based strategies, not for basic reward-driven action reinforcement. These results reveal that ACC is a critical node in model-based control, with a specific role in predicting future states given chosen actions.Akam et al. investigate mouse anterior cingulate cortex (ACC) in a sequential decision-making task, finding that ACC predicts future states given chosen actions and indicates when these predictions are violated. Transiently inhibiting ACC prevents mice from using observed state transitions to guide subsequent choices, impairing model-based reinforcement learning

Open-source, Python-based, hardware and software for controlling behavioural neuroscience experiments

Laboratory behavioural tasks are an essential research tool. As questions asked of behaviour and brain activity become more sophisticated, the ability to specify and run richly structured tasks becomes more important. An increasing focus on reproducibility also necessitates accurate communication of task logic to other researchers. To these ends, we developed pyControl, a system of open-source hardware and software for controlling behavioural experiments comprising a simple yet flexible Python-based syntax for specifying tasks as extended state machines, hardware modules for building behavioural setups, and a graphical user interface designed for efficiently running high-throughput experiments on many setups in parallel, all with extensive online documentation. These tools make it quicker, easier, and cheaper to implement rich behavioural tasks at scale. As important, pyControl facilitates communication and reproducibility of behavioural experiments through a highly readable task definition syntax and self-documenting features. Here, we outline the system’s design and rationale, present validation experiments characterising system performance, and demonstrate example applications in freely moving and head-fixed mouse behaviour

When Brains Flip Coins

In a recent study in the journal Cell, Tervo et al. (2014) show that animals can implement stochastic choice policies in environments unfavorable to predictive strategies. The shift toward stochastic behavior was driven by noradrenergic signaling in the anterior cingulate cortex

Oscillation structure determines communication accuracy.

<p>(<i>A</i>–<i>D</i>) Example firing rate modulation of the target (red) and distracting inputs (gray) over the 100 ms integration time. Gain modulation (blue) produced by the optimized receiving network. (<i>E</i>) Firing rate as a function of oscillation phase for synchronization strengths from 0.1–0.9. (<i>F</i>) Fisher information as a function of the synchronization strength of the target input for stimulus estimates decoded from receiving network output integrated over 100 ms. Distractor condition indicated by color as shown in key. (<i>G</i>) Comparison of Fisher information for asynchronous and incoherently oscillating distracting inputs as functions of firing rate of input networks. (<i>H</i>) Separation of target and distractors in frequency. Fisher information as function of oscillation frequency of distractor networks for narrowband (purple) and broadband (orange) sinusoidal oscillations and narrowband Von Mises oscillations (blue). Frequency of target input modulation (50 Hz) is indicated by black arrow. (<i>I</i>) Amplitude spectrum of oscillatory modulations for narrowband Von Mises modulation and narrow and broadband sinusoidal oscillations (F₀ is oscillation center frequency).</p

Integration time, modulation frequency and communication accuracy.

<p>(A–D) Fisher information as a function of integration time. Target modulation frequency is indicated by line color (see key). For incoherent distractors (A) and phase separation (D) conditions, distractor frequency was the same as target frequency. For frequency separation condition (C), distractor frequency is indicated by line style (see key). The duration of one period of the target input modulation is indicated by the vertical dashed lines, color coded by target modulation frequency.</p

Bottom-up coherence.

<p>(<i>A</i>) Diagram illustrating receiving network in which gain modulation is a filtered version of the summed combined spike input. (<i>B</i>–<i>E</i>) Comparison of Fisher information of decoded stimulus estimates for original ‘top-down’ model (solid lines) and ‘bottom-up’ model (dashed lines).</p

Filtering with arbitrary gain modulations.

<p>(A–D) Example input firing rate modulations, gain modulations generated by optimized linear filter (blue trace), and gain modulations found to optimize decoding accuracy for specific examples of target firing rate modulation (green traces). (E) Effect of synchronization strength on decoding accuracy for asynchronous distractors (blue), distractors oscillating incoherently in the same frequency band as the target (red) and distractors oscillating coherently with the target but equally space in phase (yellow). (F) Effect of distractor frequency on decoding accuracy. (G) Comparison of decoding accuracy for different distractor conditions indicated by color as above for synchronization strength of 0.5 and average neuronal firing rate of 5 Hz.</p