Differences in Organization of Neural Oxytocin Receptor Reflects Differences in Sex Behavior and Parental Care

Oxytocin (OXT) is a neuropeptide synthesized in the hypothalamus and stored in the posterior pituitary gland. Its most known functions include being involved in the stimulation of the uterus during childbirth and allowing for milk letdown in the process of lactation after birth. OXT has also been implicated to play a role in parental care and sex behavior. Specific binding to brain oxytocin receptors (OXTR) was observed by in vitro receptor autoradiography with an iodinated OXT analogue in the monogamous prairie vole (Microtus ochrogaster) and the polygamous montane vole (Microtus montanus). What was found was that OXTR density in the prairie vole was highest in the prelimbic cortex, bed nucleus of the stria terminalis, nucleus accumbens, midline nucleus of the thalamus, and the lateral amygdala. In contrast, OXTR density in the montane vole was highest in the lateral septum, ventromedial nucleus of the hypothalamus, and cortical nucleus of the amygdala. Prairie voles show a high level of parental care in both naïve and postpartum individuals, whereas montane voles show a much higher level of parental care postpartum. Differences in OXTR distribution were also seen in two additional species of voles, the monogamous pine vole (Microtus pinetorum) and the polygamous meadow vole (Microtus pennsylvanicus). The receptor distribution of two other neurotransmitter systems that play a role in the mediation of social behavior (benzodiazepines and µ opioids) were also studied but showed little comparable differences between monogamous and polygamous species, which further highlights the importance of OXTR distribution as a mediator of social behavior

Structured learning of assignment models for neuron reconstruction to minimize topological errors

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Structured learning provides a powerful framework for empirical risk minimization on the predictions of structured models. It allows end-to-end learning of model parameters to minimize an application specific loss function. This framework is particularly well suited for discrete optimization models that are used for neuron reconstruction from anisotropic electron microscopy (EM) volumes. However, current methods are still learning unary potentials by training a classifier that is agnostic about the model it is used in. We believe the reason for that lies in the difficulties of (1) finding a representative training sample, and (2) designing an application specific loss function that captures the quality of a proposed solution. In this paper, we show how to find a representative training sample from human generated ground truth, and propose a loss function that is suitable to minimize topological errors in the reconstruction. We compare different training methods on two challenging EM-datasets. Our structured learning approach shows consistently higher reconstruction accuracy than other current learning methods.Peer ReviewedPostprint (author's final draft

Increase of the Coefficient of Static Friction Using Turn-milling with an Inclined Milling Spindle

AbstractThere is a strong need for surfaces with a high coefficient of static friction to meet the demands for increasing performance and lightweight construction strategies, especially regarding friction-locked connections. An auspicious turn-milling process used to generate protruding surface structures which lead to a high coefficient of static friction is investigated. The influence of the corner geometry on the surface structure is examined by machining end faces of specimens of the steel 42CrMo4+QT (1.7225). Experimental tests for the identification of the coefficient of static friction show a significant increase up to 275% (μ0.1° = 0.55) for turn-milled surfaces in comparison to unstructured specimens (μ0.1 ≈ 0.2)