Decreased Interleukin-4 Release from the Neurons of the Locus Coeruleus in Response to Immobilization Stress

It has been demonstrated that immobilization (IMO) stress affects neuroimmune systems followed by alterations of physiology and behavior. Interleukin-4 (IL-4), an anti-inflammatory cytokine, is known to regulate inflammation caused by immune challenge but the effect of IMO on modulation of IL-4 expression in the brain has not been assessed yet. Here, it was demonstrated that IL-4 was produced by noradrenergic neurons in the locus coeruleus (LC) of the brain and release of IL-4 was reduced in response to IMO. It was observed that IMO groups were more anxious than nontreated groups. Acute IMO (2 h/day, once) stimulated secretion of plasma corticosterone and tyrosine hydroxylase (TH) in the LC whereas these increments were diminished in exposure to chronic stress (2 h/day, 21 consecutive days). Glucocorticoid receptor (GR), TH, and IL-4-expressing cells were localized in identical neurons of the LC, indicating that hypothalamic-pituitary-adrenal- (HPA-) axis and sympathetic-adrenal-medullary- (SAM-) axis might be involved in IL-4 secretion in the stress response. Accordingly, it was concluded that stress-induced decline of IL-4 concentration from LC neurons may be related to anxiety-like behavior and an inverse relationship exists between IL-4 secretion and HPA/SAM-axes activation

Intervention Strategies Based on Information-Motivation-Behavioral Skills Model for Health Behavior Change: A Systematic Review

SummaryPurposeThis study systematically reviewed research on behavioral interventions based on the information-motivation-behavioral skills (IMB) model to investigate specific intervention strategies that focus on information, motivation, and behavioral skills and to evaluate their effectiveness for people with chronic diseases.MethodsA systematic review was conducted in accordance with the guidelines of both the National Evidence-based Healthcare Collaborating Agency and Im and Chang. A literature search was conducted using electronic databases. Randomized controlled trials that tested behavioral interventions based on the IMB model for promoting health behaviors among people with chronic diseases were included. Four investigators independently reviewed the studies and assessed the quality of each study. A narrative synthesis was used.ResultsA total of 12 studies were included in the review. Nine studies investigated patients with HIV/AIDS. The most frequently used intervention strategies were instructional pamphlets for the information construct, motivational interviewing techniques for the motivation construct, and instruction or role playing for the behavioral skills construct. Ten studies reported significant behavior changes at the first post-intervention assessment.ConclusionThis review indicates the potential strength of the IMB model as a theoretical framework to develop behavioral interventions. The specific integration strategies delineated for each construct of the model can be utilized to design model-based interventions

Output entanglement and squeezing of two-mode fields generated by a single atom

A single four-level atom interacting with two-mode cavities is investigated. Under large detuning condition, we obtain the effective Hamiltonian which is unitary squeezing operator of two-mode fields. Employing the input-output theory, we find that the entanglement and squeezing of the output fields can be achieved. By analyzing the squeezing spectrum, we show that asymmetric detuning and asymmetric atomic initial state split the squeezing spectrum from one valley into two minimum values, and appropriate leakage of the cavity is needed for obtaining output entangled fields

Hey! I Have Something for You: Paging Cycle Based Random Access for LTE-A

The surge of M2M devices imposes new challenges for the current cellular network architecture, especially in radio access networks. One of the key issues is that the M2M traffic, characterized by small data and massive connection requests, makes significant collisions and congestion during network access via the random access (RA) procedure. To resolve this problem, in this paper, we propose a paging cycle-based protocol to facilitate the random access procedure in LTE-A. The high-level idea of our design is to leverage a UE's paging cycle as a hint to preassign RA preambles so that UEs can avoid preamble collisions at the first place. Our rpHint has two modes: (1) collision-free paging, which completely prevents cross-collision between paged user equipment (UEs) and random access UEs, and (2) collision-avoidance paging, which alleviates cross-collision. Moreover, we formulate a mathematical model to derive the optimal paging ratio that maximizes the expected number of successful UEs. This analysis also allows us to adapt dynamically to the better one between the two modes. We show via extensive simulations that our design increases the number of successful UEs in an RA procedure by more than 3× as compared to the legacy RA scheme of the LTE

High-resolution, reconfigurable printing of liquid metals with three-dimensional structures

We report an unconventional approach for high-resolution, reconfigurable 3D printing using liquid metals for stretchable, 3D integrations. A minimum line width of 1.9 ??m can be reliably formed using direct printing, and printed patterns can be reconfigured into diverse 3D structures with maintaining pristine resolutions. This reconfiguration can be performed multiple times, and it also generates a thin oxide interface that can be effective in preventing the spontaneous penetration of gallium atoms into different metal layers while preserving electrical properties under ambient conditions. Moreover, these free-standing features can be encapsulated with stretchable, conformal passivations. We demonstrate applications in the form of a reconfigurable antenna, which is tunable by changing geometeries, and reversibly movable interconnections used as mechanical switches. The free-standing 3D structure of electrodes is also advantageous for minimizing the number and space between interconnections, which is important for achieving higher integrations, as demonstrated in an array of microLEDs

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.Comment: Presented at NeurIPS 2019 Workshop "Machine Learning and the Physical Sciences