Explore the vast range of titles available.

physical overlap between dendrites and axons constrain the circuit topology, and the precise shape and composition of dendrites determine the integration of inputs to produce an output signal. At the same time, morphologies are highly diverse and variant. The variance, presumably, originates from neurons developing in a densely packed brain substrate where they interact (e.g., repulsion or attraction) with other actors in this substrate. However, when studying neurons their context is never part of the analysis and they are treated as if they existed in isolation. Here we argue that to fully understand neuronal morphology and its variance it is important to consider neurons in relation to each other and to other actors in the surrounding brain substrate, i.e., their context. We propose a context-aware computational framework, NeuroMaC, in which large numbers of neurons can be grown simultaneously according to growth rules expressed in terms of interactions between the developing neuron and the surrounding brain substrate. As a proof of principle, we demonstrate that by using NeuroMaC we can generate accurate virtual morphologies of distinct classes both in isolation and as part of neuronal forests. Accuracy is validated against population statistics of experimentally reconstructed morphologies. We show that context-aware generation of neurons can explain characteristics of variation. Indeed, plausible variation is an inherent property of the morphologies generated by context-aware rules. We speculate about the applicability of this framework to investigate morphologies and circuits, to classify healthy and pathological morphologies, and to generate large quantities of morphologies for large-scale modeling.

The global seagrass decline has prompted numerous restoration efforts to reverse current trends. Yet, restoration efforts are challenged by ecological feedbacks and prevalent stressors. Identifying these stressors and the thresholds where seagrass shoot production becomes negative is vital to improve site-selection procedures and increase restoration success. In this study, we investigated the ecological compensation irradiance (ECI) and depth limit of eelgrass ( Zostera marina L) transplants along a eutrophication gradient. This was accomplished by establishing eelgrass transplants along eutrophication and depth gradients while continuously measuring benthic Photosynthetically Active Radiation (PAR). High-temporal monitoring of shoot count allowed precise estimates of shoot production, which was applied to modified photosynthesis-irradiance curves, thereby estimating the ECI. The ECI fell within the interval 2.6 – 9.8 E m -2 d -1 and responded distinctly along the eutrophication gradient, decreasing as eutrophication and nutrient-derived stressors were alleviated. The depth limits were concurrently controlled by irradiance and ECI and similarly responded along the eutrophication gradient, increasing from 1.1 m at the innermost station to 4.7 – 5.6 m at the two outermost least eutrophic stations. The results demonstrate that the ECI of eelgrass varies according to the local environment, with implications for habitat suitability assessment and site selection procedures in restoration efforts.

Dendrite arbor morphology is critical for neuron function. Yalgin and colleagues find that the activity of Centrosomin, used to build the mitotic spindle, is recycled after mitosis in dendrites. Centrosomin shapes the arbor by engaging microtubule nucleation at dendritic Golgi outposts to orient microtubule polarization in nascent branches. Neuronal dendrite branching is fundamental for building nervous systems. Branch formation is genetically encoded by transcriptional programs to create dendrite arbor morphological diversity for complex neuronal functions. In Drosophila sensory neurons, the transcription factor Abrupt represses branching via an unknown effector pathway. Targeted screening for branching-control effectors identified Centrosomin, the primary centrosome-associated protein for mitotic spindle maturation. Centrosomin repressed dendrite branch formation and was used by Abrupt to simplify arbor branching. Live imaging revealed that Centrosomin localized to the Golgi cis face and that it recruited microtubule nucleation to Golgi outposts for net retrograde microtubule polymerization away from nascent dendrite branches. Removal of Centrosomin enabled the engagement of wee Augmin activity to promote anterograde microtubule growth into the nascent branches, leading to increased branching. The findings reveal that polarized targeting of Centrosomin to Golgi outposts during elaboration of the dendrite arbor creates a local system for guiding microtubule polymerization.

It is commonly accepted that the Inferior Olive (IO) provides a timing signal to the cerebellum. Stable subthreshold oscillations in the IO can facilitate accurate timing by phase-locking spikes to the peaks of the oscillation. Several theoretical models accounting for the synchronized subthreshold oscillations have been proposed, however, two experimental observations remain an enigma. The first is the observation of frequent alterations in the frequency of the oscillations. The second is the observation of constant phase differences between simultaneously recorded neurons. In order to account for these two observations we constructed a canonical network model based on anatomical and physiological data from the IO. The constructed network is characterized by clustering of neurons with similar conductance densities, and by electrical coupling between neurons. Neurons inside a cluster are densely connected with weak strengths, while neurons belonging to different clusters are sparsely connected with stronger connections. We found that this type of network can robustly display stable subthreshold oscillations. The overall frequency of the network changes with the strength of the inter-cluster connections, and phase differences occur between neurons of different clusters. Moreover, the phase differences provide a mechanistic explanation for the experimentally observed propagating waves of activity in the IO. We conclude that the architecture of the network of electrically coupled neurons in combination with modulation of the inter-cluster coupling strengths can account for the experimentally observed frequency changes and the phase differences.

Neurons of the cerebellar nuclei convey the final output of the cerebellum to their targets in various parts of the brain. Within the cerebellum their direct upstream connections originate from inhibitory Purkinje neurons. Purkinje neurons have a complex firing pattern of regular spikes interrupted by intermittent pauses of variable length. How can the cerebellar nucleus process this complex input pattern? In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. Pause beginning synchronization produced a unique effect on nuclei neuron firing, while the effect of pause ending and pause overlapping synchronization could not be distinguished from each other. Pause beginning synchronization produced better time-locking of nuclear neurons for short length pauses. We also characterize the effect of pause length and spike jitter on the nuclear neuron firing. Additionally, we find that the rate of rebound responses in nuclear neurons after a synchronous pause is controlled by the firing rate of Purkinje neurons preceding it.

An important task performed by a neuron is the selection of relevant inputs from among thousands of synapses impinging on the dendritic tree. Synaptic plasticity enables this by strenghtening a subset of synapses that are, presumably, functionally relevant to the neuron. A different selection mechanism exploits the resonance of the dendritic membranes to preferentially filter synaptic inputs based on their temporal rates. A widely held view is that a neuron has one resonant frequency and thus can pass through one rate. Here we demonstrate through mathematical analyses and numerical simulations that dendritic resonance is inevitably a spatially distributed property; and therefore the resonance frequency varies along the dendrites, and thus endows neurons with a powerful spatiotemporal selection mechanism that is sensitive both to the dendritic location and the temporal structure of the incoming synaptic inputs.

High-entropy alloys are formed by synthesizing five or more principal elements in equimolar or near equimolar concentrations. Microstructure of the CoCrCuFeNiAl0.5Bx (x = 0, 0.2, 0.6, 1) high-entropy alloys under investigation is composed of a mixture of disordered bcc and fcc phases and borides. These alloys were tested gravimetrically for their corrosion resistance in simulated syngas containing 0, 0.01, 0.1, and 1 % H2S at 500 °C. The exposed coupons were characterized using XRD and SEM. No significant corrosion was detected at 500 °C in syngas containing 0 and 0.01 % H2S while significant corrosion was observed in syngas containing 0.1 and 1 % H2S. Cu1.96S was the primary sulfide in the external corrosion scale on the low-boron high-entropy alloys, whereas FeCo4Ni4S8 on the high-boron high-entropy alloys. Multi-phase Cu-rich regions in the low-B high-entropy alloys were vulnerable to corrosive attack.

Several efficient procedures exist to digitally trace neuronal structure from light microscopy, and mature community resources have emerged to store, share, and analyze these datasets. In contrast, the quantification of intracellular distributions and morphological dynamics is not yet standardized. Current widespread descriptions of neuron morphology are static and inadequate for subcellular characterizations. We introduce a new file format to represent multichannel information as well as an open-source Vaa3D plugin to acquire this type of data. Next we define a novel data structure to capture morphological dynamics, and demonstrate its application to different time-lapse experiments. Importantly, we designed both innovations as judicious extensions of the classic SWC format, thus ensuring full back-compatibility with popular visualization and modeling tools. We then deploy the combined multichannel/time-varying reconstruction system on developing neurons in live Drosophila larvae by digitally tracing fluorescently labeled cytoskeletal components along with overall dendritic morphology as they changed over time. This same design is also suitable for quantifying dendritic calcium dynamics and tracking arbor-wide movement of any subcellular substrate of interest.