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An organizational feature of neural circuits is the specificity of synaptic connections. A striking example is the direction-selective (DS) circuit of the retina. There are multiple subtypes of DS retinal ganglion cells (DSGCs) that prefer motion along one of 4 preferred directions. This computation is mediated by selective wiring of a single inhibitory interneuron, the starburst amacrine cell (SAC), with each DSGC subtype preferentially receiving input from a subset of SAC processes. We hypothesize that the molecular basis of this wiring is mediated in part by unique expression profiles of DSGC subtypes. To test this, we first performed paired recordings from isolated mouse retina of both sexes to determine that postnatal day 10 (P10) represents the age at which asymmetric synapses form. Second, we performed RNA-sequencing and differential expression analysis on isolated P10 ON-OFF DSGCs tuned for either nasal or ventral motion and identified candidates which may promote direction-specific wiring. We then used a conditional knockout strategy to test the role of one candidate, the secreted synaptic organizer cerebellin-4 (Cbln4), in the development of DS tuning. Using two-photon calcium imaging, we observed a small deficit in directional tuning among ventral-preferring DSGCs lacking Cbln4, though whole-cell voltage clamp recordings did not identify a significant change in inhibitory inputs. This suggests that Cbln4 does not function primarily via a cell-autonomous mechanism to instruct wiring of DS circuits. Nevertheless, our transcriptomic analysis identified unique candidate factors for gaining insights into the molecular mechanisms that instruct wiring specificity in the DS circuit.

Significance StatementBy performing mRNA transcriptome analysis on three populations of direction-selective ganglion cells - two preferring horizontal motion and one preferring vertical motion - we identified differentially expressed candidate molecules potentially involved in cell subtype-specific synaptogenesis within this circuit. We tested the role of one differentially expressed candidate, Cbln4, enriched in ventral-preferring DSGCs. Using a targeted knockout approach, the deletion of Cbln4 led to a small reduction in direction-selective tuning while maintaining dendritic morphology and normal strength and asymmetry of inhibitory synaptic transmission. Overall, we have shown that this approach can be used to identify interesting candidate molecules, and future functional studies are required to reveal the mechanisms by which these candidates influence synaptic wiring within specific circuits.

 

Key Points Detailed analysis of spectral transition of a Stable Auroral Red (SAR) Arc into Strong Thermal Emission Velocity Enhancement (STEVE) emission Ionospheric threshold conditions may be a requirement for the evolution of STEVE Basic parameters of transition features from SAR Arc to STEVE presented 

CONTEXT For the last 40 years, the aggregate number of women receiving bachelor’s degrees in engineering in the US has remained stuck at approximately 20%. Research into this “disappointing state of affairs” has established that “the [educational] institutions in which women sought inclusion are themselves gendered, raced and classed” (Borrego, 2011; Riley et al., 2015; Tonso, 2007). PURPOSE Our focus is women students who thrive in undergraduate engineering student project teams. We need to learn more about how they describe becoming an engineer, about how women come to think of themselves as engineers and about how they perform their engineering selves, and how others come to identify them as engineers (Tonso, 2006). METHODS We are guided by a feminist, activist, and interpretive lens. Our multi-case study method, i.e., three semi-structured interviews and photovoice, offers two advantages: 1) the knowledge generated by case studies is concrete and context dependent (Case and Light, 2011); 2) case studies are useful in the heuristic identification of new variables and potential hypotheses (George and Bennett, 2005). ACTUAL OUTCOMES Our preliminary results suggest these women find joy in their experience of developing and applying engineering expertise to real, tangible, and challenging problems. They find knowing-about and knowing-how exciting, self-rewarding and self-defining. Further, these women work to transform the culture or ways of participating in project teams. This transforming not only facilitates knowing-about and knowing-how; but also it creates an environment in which women can claim their expertise, their identity as engineers, and have those expertise and identities affirmed by others. CONCLUSIONS If we aim to transform our gendered, raced, classed institutions, we need to learn more about women who thrive within those institutions. We need to learn more about the joy of doing engineering that these women experience. We also need to learn more about how they create an “integration-and-learning perspective” for themselves (Ely and Thomas, 2001) and a “climate for inclusion” within those project teams (Nishii, 2012), a perspective and climate that fosters the joy of doing engineering. 

As autonomous robots interact and navigate around real-world environments such as homes, it is useful to reliably identify and manipulate articulated objects, such as doors and cabinets. Many prior works in object articulation identification require manipulation of the object, either by the robot or a human. While recent works have addressed predicting articulation types from visual observations alone, they often assume prior knowledge of category-level kinematic motion models or sequence of observations where the articulated parts are moving according to their kinematic constraints. In this work, we propose FormNet, a neural network that identifies the articulation mechanisms between pairs of object parts from a single frame of an RGB-D image and segmentation masks. The network is trained on 100k synthetic images of 149 articulated objects from 6 categories. Synthetic images are rendered via a photorealistic simulator with domain randomization. Our proposed model predicts motion residual flows of object parts, and these flows are used to determine the articulation type and parameters. The network achieves an articulation type classification accuracy of 82.5% on novel object instances in trained categories. Experiments also show how this method enables generalization to novel categories and can be applied to real-world images without fine-tuning. 

Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e.g., sliding an object to a goal pose while maintaining con- tact with a table. Individual subtasks can be achieved by task-axis controllers defined relative to the objects being manipulated, and a set of object-centric controllers can be combined in an hierarchy. In prior works, such combinations are defined manually or learned from demonstrations. By contrast, we propose using reinforcement learning to dynamically compose hierarchical object-centric controllers for manipulation tasks. Experiments in both simulation and real world show how the proposed approach leads to improved sample efficiency, zero-shot generalization to novel test environments, and simulation-to-reality transfer with- out fine-tuning.