Glucose Enhances Leptin Signaling through Modulation of AMPK Activity

Leptin exerts its action by binding to and activating the long form of leptin receptors (LEPRb). LEPRb activates JAK2 that subsequently phosphorylates and activates STAT3. The JAK2/STAT3 pathway is required for leptin control of energy balance and body weight. Defects in leptin signaling lead to leptin resistance, a primary risk factor for obesity. Body weight is also regulated by nutrients, including glucose. Defects in glucose sensing also contribute to obesity. Here we report crosstalk between leptin and glucose. Glucose starvation blocked the ability of leptin to stimulate tyrosyl phosphorylation and activation of JAK2 and STAT3 in a variety of cell types. Glucose dose-dependently enhanced leptin signaling. In contrast, glucose did not enhance growth hormone-stimulated phosphorylation of JAK2 and STAT5. Glucose starvation or 2-deoxyglucose-induced inhibition of glycolysis activated AMPK and inhibited leptin signaling; pharmacological inhibition of AMPK restored the ability of leptin to stimulate STAT3 phosphorylation. Conversely, pharmacological activation of AMPK was sufficient to inhibit leptin signaling and to block the ability of glucose to enhance leptin signaling. These results suggest that glucose and/or its metabolites play a permissive role in leptin signaling, and that glucose enhances leptin sensitivity at least in part by attenuating the ability of AMPK to inhibit leptin signaling

Rapid Determination of Saponins in the Honey-Fried Processing of Rhizoma Cimicifugae by Near Infrared Diffuse Reflectance Spectroscopy.

ObjectiveA model of Near Infrared Diffuse Reflectance Spectroscopy (NIR-DRS) was established for the first time to determine the content of Shengmaxinside I in the honey-fried processing of Rhizoma Cimicifugae.MethodsShengmaxinside I content was determined by high-performance liquid chromatography (HPLC), and the data of the honey-fried processing of Rhizoma Cimicifugae samples from different batches of different origins by NIR-DRS were collected by TQ Analyst 8.0. Partial Least Squares (PLS) analysis was used to establish a near-infrared quantitative model.ResultsThe determination coefficient R² was 0.9878. The Cross-Validation Root Mean Square Error (RMSECV) was 0.0193%, validating the model with a validation set. The Root Mean Square Error of Prediction (RMSEP) was 0.1064%. The ratio of the standard deviation for the validation samples to the standard error of prediction (RPD) was 5.5130.ConclusionThis method is convenient and efficient, and the experimentally established model has good prediction ability, and can be used for the rapid determination of Shengmaxinside I content in the honey-fried processing of Rhizoma Cimicifugae

Pricing Decision of Closed-Loop Supply Chain to Improve Service Level under Patent Protection

This paper constructs a two-level closed-loop supply chain system consisting of original parts manufacturers and parts distributors. Based on the different preferences of consumers for remanufactured parts and new parts, four combination models of patent protection and service improvement are constructed. Through comparative analysis, the impact of implementing patent protection policies by original parts manufacturers and improving service levels by parts distributors on the pricing decisions of the closed-loop supply chain is explored.Through the comparison between related models and the verification of calculation examples, it is found that (1) a manufacturer prevents the price of new products from being affected by the price of remanufactured products and upgrading of service level by introducing royalties, which reduces its loss of profit; (2) in the absence of patent protection, the manufacturerꞌs profit decreases as the level of service increases; in the presence of patent protection, the manufacturerꞌs profit increases as the level of service increases; (3) retailersꞌ profits decrease after the manufacturer introduces royalties, which discourages them to improve service levels for remanufactured products; (4) as retailers raise the service level of the remanufactured products, the profits of the manufacturer and third-party manufacturers keep increasing, while the profits of the retailers first increase and then decrease

EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System

Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as medical emergencies and fire outbreaks in urban areas. Existing methods for EMV dispatch typically optimize routes based on historical traffic-flow data and design traffic signal pre-emption accordingly; however, we still lack a systematic methodology to address the coupling between EMV routing and traffic signal control. In this paper, we propose EMVLight, a decentralized reinforcement learning (RL) framework for joint dynamic EMV routing and traffic signal pre-emption. We adopt the multi-agent advantage actor-critic method with policy sharing and spatial discounted factor. This framework addresses the coupling between EMV navigation and traffic signal control via an innovative design of multi-class RL agents and a novel pressure-based reward function. The proposed methodology enables EMVLight to learn network-level cooperative traffic signal phasing strategies that not only reduce EMV travel time but also shortens the travel time of non-EMVs. Simulation-based experiments indicate that EMVLight enables up to a $42.6\%$ reduction in EMV travel time as well as an $23.5\%$ shorter average travel time compared with existing approaches.Comment: 19 figures, 10 tables. Manuscript extended on previous work arXiv:2109.05429, arXiv:2111.0027

Mouse hepatocyte overexpression of NF‐κB‐inducing kinase (NIK) triggers fatal macrophage‐dependent liver injury and fibrosis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109646/1/hep27348-sup-0001-suppinfo01.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109646/2/hep27348.pd

The molecular clouds in a section of the third Galactic quadrant: observational properties and chemical abundance ratio between CO and its isotopologues

We compare the observational properties between $^{12}$CO, $^{13}$CO, and C$^{18}$O and summarize the observational parameters based on 7069 clouds sample from the Milky Way Imaging Scroll Painting (MWISP) CO survey in a section of the third Galactic quadrant. We find that the $^{13}$CO angular area ($A_{\rm ^{13}CO}$) generally increases with that of $^{12}$CO ($A_{\rm ^{12}CO}$), and the ratio of $A_{\rm ^{13}CO}$ to $A_{\rm ^{12}CO}$ is 0.38 by linear fitting. We find that the $^{12}$CO and $^{13}$CO flux are tightly correlated as $F_{\rm ^{13}CO}~=~0.17~ F_{\rm ^{12}CO}$ with both fluxes calculated within the $^{13}$CO-bright region. This indicates that the abundance $X_{\rm ^{13}CO}$ is a constant to be 6.5$^{+0.1}_{-0.5}$ $\times 10^{-7}$ for all samples under assumption of local thermodynamic equilibrium (LTE). Additionally, we observed that the X-factor is approximately constant in large sample molecular clouds. Similarly, we find $F_{\rm C^{18}O}~=~0.11~F_{\rm ^{13}CO}$ with both fluxes calculated within C$^{18}$O-bright region, which indicates that the abundance ratios ${X_{\rm ^{13}CO}/X_{\rm C^{18}O}}$ stays the same value 9.7$^{+0.6}_{-0.8}$ across the molecular clouds under LTE assumption. The linear relationships of $F_{\rm ^{12}CO}$ vs. $F_{\rm ^{13}CO}$ and $F_{\rm ^{13}CO}$ vs. $F_{\rm C^{18}O}$ hold not only for the $^{13}$CO-bright region or C$^{18}$O-bright region, but also for the entire molecular cloud scale with lower flux ratio. The abundance ratio ${X_{\rm ^{13}CO}/X_{\rm C^{18}O}}$ inside clouds shows a strong correlation with column density and temperature. This indicates that the ${X_{\rm ^{13}CO}/X_{\rm C^{18}O}}$ is dominated by a combination of chemical fractionation, selectively dissociation, and self-shielding effect inside clouds.Comment: 11 pages, 16 figures, 1 table, accepted by A

Anomalous Floquet non-Hermitian skin effect in a ring resonator lattice

We present a one-dimensional coupled ring resonator lattice exhibiting a variant of the non- Hermitian skin effect (NHSE) that we call the anomalous Floquet NHSE. Unlike existing approaches to achieving the NHSE by engineering gain and loss on different ring segments, our design uses fixed on-site gain or loss in each ring. The anomalous Floquet NHSE is marked by the existence of skin modes at every value of the Floquet quasienergy, allowing for broadband asymmetric transmission. Varying the gain/loss induces a non-Hermitian topological phase transition, reversing the localization direction of the skin modes. An experimental implementation in an acoustic lattice yields good agreement with theoretical predictions, with a very broad relative bandwidth of around 40%.Comment: 7 pages, 3 figure

Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels

Recent studies on contrastive learning have achieved remarkable performance solely by leveraging few labels in the context of medical image segmentation. Existing methods mainly focus on instance discrimination and invariant mapping. However, they face three common pitfalls: (1) tailness: medical image data usually follows an implicit long-tail class distribution. Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention. This motivates us to seek a principled approach for strategically making use of the dataset itself to discover similar yet distinct samples from different anatomical views. In this paper, we introduce a novel semi-supervised 2D medical image segmentation framework termed Mine yOur owN Anatomy (MONA), and make three contributions. First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features, mainly due to lacking the supervision signal. We show two simple solutions towards learning invariances - through the use of stronger data augmentations and nearest neighbors. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features in an unsupervised manner. Lastly, our extensive results on three benchmark datasets with different labeled settings validate the effectiveness of our proposed MONA which achieves new state-of-the-art under different labeled settings