Animals need to flexibly respond to stimuli from their environment without compromising behavioural consistency. For example, female crickets orienting toward a conspecific male's calling song in search of a mating partner need to stay responsive to other signals that provide information about obstacles and predators. Here, we investigate how spontaneously walking crickets and crickets engaging in acoustically guided goal-directed navigation, i.e. phonotaxis, respond to mechanosensory stimuli detected by their long antennae. We monitored walking behaviour of female crickets on a trackball during lateral antennal stimulation, which was achieved by moving a wire mesh transiently into reach of one antenna. During antennal stimulation alone, females reduced their walking speed, oriented toward the object and actively explored it with antennal movements. Additionally, some crickets initially turned away from the approaching object. Females responded in a similar way when the antennal stimulus was presented during ongoing phonotaxis: forward velocity was reduced and phonotactic steering was suppressed while the females turned toward and explored the object. Further, rapid steering bouts to individual chirps, typical for female phonotaxis, no longer occurred.Our data reveals that in this experimental situation antennal stimulation overrides phonotaxis for extended time periods. Phonotaxis in natural environments, which require the integration of multiple sensory cues, may therefore be more variable than phonotaxis measured under ideal laboratory conditions. Combining this new behavioural paradigm with neurophysiological methods will show where the sensory-motor integration of antennal and acoustic stimulation occurs and how this is achieved on a mechanistic level.

The visual allure of microscopy makes it an intuitively powerful research tool. Intuition, however, can easily obscure or distort the reality of the information contained in an image. Common cognitive biases, combined with institutional pressures that reward positive research results, can quickly skew a microscopy project towards upholding, rather than rigorously challenging, a hypothesis. The impact of these biases on a variety of research topics is well known. What might be less appreciated are the many forms in which bias can permeate a microscopy experiment. Even well-intentioned researchers are susceptible to bias, which must therefore be actively recognized to be mitigated. Importantly, although image quantification has increasingly become an expectation, ostensibly to confront subtle biases, it is not a guarantee against bias and cannot alone shield an experiment from cognitive distortions. Here, we provide illustrative examples of the insidiously pervasive nature of bias in microscopy experiments - from initial experimental design to image acquisition, analysis and data interpretation. We then provide suggestions that can serve as guard rails against bias.

A pathogenetic feature of Alzhemier disease is the aggregation of monomeric beta-amyloid proteins (Abeta) to form oligomers. Usually these oligomers of long peptides aggregate on time scales of microseconds or longer, making computational studies using atomistic molecular dynamics models prohibitively expensive and making it essential to develop computational models that are cheaper and at the same time faithful to physical features of the process. We benchmark the ability of our implicit solvent model to describe equilibrium and dynamic properties of monomeric Abeta(10-35) using all-atom Langevin dynamics (LD) simulations, since Alphabeta(10-35) is the only fragment whose monomeric properties have been measured. The accuracy of the implicit solvent model is tested by comparing its predictions with experiment and with those from a new explicit water MD simulation, (performed using CHARMM and the TIP3P water model) which is approximately 200 times slower than the implicit water simulations. The dependence on force field is investigated by running multiple trajectories for Alphabeta(10-35) using the CHARMM, OPLS-aal, and GS-AMBER94 force fields, whereas the convergence to equilibrium is tested for each force field by beginning separate trajectories from the native NMR structure, a completely stretched structure, and from unfolded initial structures. The NMR order parameter, S2, is computed for each trajectory and is compared with experimental data to assess the best choice for treating aggregates of Alphabeta. The computed order parameters vary significantly with force field. Explicit and implicit solvent simulations using the CHARMM force fields display excellent agreement with each other and once again support the accuracy of the implicit solvent model. Alphabeta(10-35) exhibits great flexibility, consistent with experiment data for the monomer in solution, while maintaining a general strand-loop-strand motif with a solvent-exposed hydrophobic patch that is believed to be important for aggregation. Finally, equilibration of the peptide structure requires an implicit solvent LD simulation as long as 30 ns.

A microscope has a light source for generating a light beam having a wavelength, λ, and beam-forming optics configured for receiving the light beam and generating a Bessel-like beam that is directed into a sample. The beam-forming optics include an excitation objective having an axis oriented in a first direction. Imaging optics are configured for receiving light from a position within the sample that is illuminated by the Bessel-like beam and for imaging the received light on a detector. The imaging optics include a detection objective having an axis oriented in a second direction that is non-parallel to the first direction. A detector is configured for detecting signal light received by the imaging optics, and an aperture mask is positioned.

Circular RNAs (circRNAs) are formed in all domains of life and via different mechanisms. There has been an explosion in the number of circRNA papers in recent years; however, as a relatively young field, circRNA biology has an urgent need for common experimental standards for isolating, analyzing, expressing and depleting circRNAs. Here we propose a set of guidelines for circRNA studies based on the authors’ experience. This Perspective will specifically address the major class of circRNAs in Eukarya that are generated by a spliceosome-catalyzed back-splicing event. We hope that the implementation of best practice principles for circRNA research will help move the field forward and allow a better functional understanding of this fascinating group of RNAs.

This paper provides an overview of the discussion and presentations from the Workshop on the Management of Large CryoEM Facilities held at the New York Structural Biology Center, New York, NY on February 6–7, 2017. A major objective of the workshop was to discuss best practices for managing cryoEM facilities. The discussions were largely focused on supporting single-particle methods for cryoEM and topics included: user access, assessing projects, workflow, sample handling, microscopy, data management and processing, and user training.

Sample size is a critical component in the design of any high-throughput genetic screening approach. Sample size determination from assumptions or limited data at the planning stages, though standard practice, may at times be unreliable because of the difficulty of a priori modeling of effect sizes and variance. Methods to update the sample size estimate during the course of the study could improve statistical power. In this article, we introduce an approach to estimate the power and update it continuously during the screen. We use this estimate to decide where to sample next to achieve maximum overall statistical power. Finally, in simulations, we demonstrate significant gains in study recall over the naive strategy of equal sample sizes while maintaining the same total number of samples.

NMDA receptors (NMDARs) are classically known as coincidence detectors for the induction of long-term synaptic plasticity and have been implicated in hippocampal CA3 cell-dependent spatial memory functions that likely rely on dynamic cellular ensemble encoding of space. The unique functional properties of both NMDARs and mossy fiber projections to CA3 pyramidal cells place mossy fiber NMDARs in a prime position to influence CA3 ensemble dynamics. By mimicking presynaptic and postsynaptic activity patterns observed in vivo, we found a burst timing-dependent pattern of activity that triggered bidirectional long-term NMDAR plasticity at mossy fiber-CA3 synapses in rat hippocampal slices. This form of plasticity imparts bimodal control of mossy fiber-driven CA3 burst firing and spike temporal fidelity. Moreover, we found that mossy fiber NMDARs mediate heterosynaptic metaplasticity between mossy fiber and associational-commissural synapses. Thus, bidirectional NMDAR plasticity at mossy fiber-CA3 synapses could substantially contribute to the formation, storage and recall of CA3 cell assembly patterns.

Learning requires neural adaptations thought to be mediated by activity-dependent synaptic plasticity. A relatively non-standard form of synaptic plasticity driven by dendritic calcium spikes, or plateau potentials, has been reported to underlie place field formation in rodent hippocampal CA1 neurons. Here we found that this behavioral timescale synaptic plasticity (BTSP) can also reshape existing place fields via bidirectional synaptic weight changes that depend on the temporal proximity of plateau potentials to pre-existing place fields. When evoked near an existing place field, plateau potentials induced less synaptic potentiation and more depression, suggesting BTSP might depend inversely on postsynaptic activation. However, manipulations of place cell membrane potential and computational modeling indicated that this anti-correlation actually results from a dependence on current synaptic weight such that weak inputs potentiate and strong inputs depress. A network model implementing this bidirectional synaptic learning rule suggested that BTSP enables population activity, rather than pairwise neuronal correlations, to drive neural adaptations to experience.