Ikkε Is Key To Induction Of Insulin Resistance In The Hypothalamus, And Its Inhibition Reverses Obesity.

IKK epsilon (IKKε) is induced by the activation of nuclear factor-κB (NF-κB). Whole-body IKKε knockout mice on a high-fat diet (HFD) were protected from insulin resistance and showed altered energy balance. We demonstrate that IKKε is expressed in neurons and is upregulated in the hypothalamus of obese mice, contributing to insulin and leptin resistance. Blocking IKKε in the hypothalamus of obese mice with CAYMAN10576 or small interfering RNA decreased NF-κB activation in this tissue, relieving the inflammatory environment. Inhibition of IKKε activity, but not TBK1, reduced IRS-1(Ser307) phosphorylation and insulin and leptin resistance by an improvement of the IR/IRS-1/Akt and JAK2/STAT3 pathways in the hypothalamus. These improvements were independent of body weight and food intake. Increased insulin and leptin action/signaling in the hypothalamus may contribute to a decrease in adiposity and hypophagia and an enhancement of energy expenditure accompanied by lower NPY and increased POMC mRNA levels. Improvement of hypothalamic insulin action decreases fasting glycemia, glycemia after pyruvate injection, and PEPCK protein expression in the liver of HFD-fed and db/db mice, suggesting a reduction in hepatic glucose production. We suggest that IKKε may be a key inflammatory mediator in the hypothalamus of obese mice, and its hypothalamic inhibition improves energy and glucose metabolism.

Emergent Antiferromagnetism Out Of The Hidden-order" State In Uru2si2: High Magnetic Field Nuclear Magnetic Resonance To 40 T."

Very high field (29)Si-NMR measurements using a fully (29)Si-enriched URu(2)Si(2) single crystal were carried out in order to microscopically investigate the hidden order (HO) state and adjacent magnetic phases in the high field limit. At the lowest measured temperature of 0.4 K, a clear anomaly reflecting a Fermi surface instability near 22 T inside the HO state is detected by the (29)Si shift, (29)K(c). Moreover, a strong enhancement of (29)K(c) develops near a critical field H(c) ≃ 35.6 T, and the ^{29}Si-NMR signal disappears suddenly at H(c), indicating the total suppression of the HO state. Nevertheless, a weak and shifted (29)Si-NMR signal reappears for fields higher than H(c) at 4.2 K, providing evidence for a magnetic structure within the magnetic phase caused by the Ising-type anisotropy of the uranium ordered moments.

Bipolar Tribocharging Signal During Friction Force Fluctuations At Metal-insulator Interfaces.

Friction and triboelectrification of materials show a strong correlation during sliding contacts. Friction force fluctuations are always accompanied by two tribocharging events at metal-insulator [e.g., polytetrafluoroethylene (PTFE)] interfaces: injection of charged species from the metal into PTFE followed by the flow of charges from PTFE to the metal surface. Adhesion maps that were obtained by atomic force microscopy (AFM) show that the region of contact increases the pull-off force from 10 to 150 nN, reflecting on a resilient electrostatic adhesion between PTFE and the metallic surface. The reported results suggest that friction and triboelectrification have a common origin that must be associated with the occurrence of strong electrostatic interactions at the interface.

Uso de redes neurais artificiais na identificação de vocalização de suínos

Animal welfare has been an important research topic in animal production mainly in its ways of assessment. Vocalization is found to be an interesting tool for evaluating welfare as it provides data in a non-invasive way as well as it allows easy automation of process. The present research had as objective the implementation of an algorithm based on artificial neural network that had the potential of identifying vocalization related to welfare pattern indicatives. The research was done in two parts, the first was the development of the algorithm, and the second its validation with data from the field. Previous records allowed the development of the algorithm from behaviors observed in sows housed in farrowing cages. Matlab® software was used for implementing the network. It was selected a retropropagation gradient algorithm for training the network with the following stop criteria: maximum of 5,000 interactions or error quadratic addition smaller than 0.1. Validation was done with sows and piglets housed in commercial farm. Among the usual behaviors the ones that deserved enhancement were: the feed dispute at farrowing and the eventual risk of involuntary aggression between the piglets or between those and the sow. The algorithm was able to identify through the noise intensity the inherent risk situation of piglets welfare reduction.