Vitamin B1 Helps to Limit Mycobacterium tuberculosis Growth via Regulating Innate Immunity in a Peroxisome Proliferator-Activated Receptor-γ-Dependent Manner

It is known that vitamin B1 (VB1) has a protective effect against oxidative retinal damage induced by anti-tuberculosis drugs. However, it remains unclear whether VB1 regulates immune responses during Mycobacterium tuberculosis (MTB) infection. We report here that VB1 promotes the protective immune response to limit the survival of MTB within macrophages and in vivo through regulation of peroxisome proliferator-activated receptor γ (PPAR-γ). VB1 promotes macrophage polarization into classically activated phenotypes with strong microbicidal activity and enhanced tumor necrosis factor-α and interleukin-6 expression at least in part by promoting nuclear factor-κB signaling. In addition, VB1 increases mitochondrial respiration and lipid metabolism and PPAR-γ integrates the metabolic and inflammatory signals regulated by VB1. Using both PPAR-γ agonists and deficient mice, we demonstrate that VB1 enhances anti-MTB activities in macrophages and in vivo by down-regulating PPAR-γ activity. Our data demonstrate important functions of VB1 in regulating innate immune responses against MTB and reveal novel mechanisms by which VB1 exerts its function in macrophages

Research and Translation for China Law and Society Review Special Edition

http://deepblue.lib.umich.edu/bitstream/2027.42/172488/1/Wang, Chuxuan_Capstone Essay (1).pdfDescription of Wang, Chuxuan_Capstone Essay (1).pdf : Capstone EssaySEL

Machine-Learning-Aided Optical Fiber Communication System

The fiber optical network offers high speed, large bandwidth, and a high degree of reliability. However, the development of optical communication technology has hit a bottleneck due to several challenges such as energy loss, cost, and system capacity approaching the Shannon limit. As a powerful tool, machine learning technology provides a strong driving force for the development of various industries and greatly promotes the development of society. Machine learning also provides a new possible solution to achieve greater transmission capacities and longer transmission distances in optical communications. In this article, we introduce the application of machine learning in optical communication network systems. Three use cases are presented to evaluate the feasibility of our proposed architecture. In the transmission layer, the principal-component-based phase estimation algorithm is used for phase noise recovery in coherent optical systems, and the K-means algorithm is adopted to reduce the influence of nonlinear noise in probabilistic shaping systems. As for the network layer, the long short-term memory algorithm and the genetic algorithm are suitable for making traffic predictions and determining reasonable placement locations of remote radio heads in centralized radio access networks. Extensive simulations and experiments are conducted to evaluate the proposed algorithm in comparison to the state-of-the-art schemes. The results show the performance of three use cases. Machine learning algorithms applied to the transmission layer can greatly promote the performance of digital signal processing without increasing the complexity. Machine learning algorithms applied to the network layer can provide a more appropriate channel allocation plan in the era of high-speed communication. Ultimately, the intent of this article is to serve as a basis for stimulating more research in machine learning in optical communications

Prediction of Stope Stability Using Variable Weight and Unascertained Measurement Technique

A new model is established to analyze mining stope stability, using variable weight theory to calculate the index weight for each factor in different stopes and unascertained measure evaluation technique to predict the risk grade of stope stability. In this model, an evaluation index system by virtue of the 7 most important factors is established, including rock saturated uniaxial compressive strength, rock quality designation, rock joint and fissure, stope span, condition of pillar, groundwater seepage volume, and rate of supporting pit roof. And each index is divided into 5 grades by assignment value and the classification method of standardization. Accordingly, the analysis result is also classified into 5 risk grades. This model is used for the 6 main stopes from the -270 m section in Xin-Qiao Mine, China. The results, giving risk grade for each stope and guiding the use of corresponding measures, avoided the problem of state out of balance caused by conventional invariable weight theory models and have ensured no accident occurred in mining production in recent years. This model can be used in other mines widely, by assigning values for the 7 factors on basis of current in situ cases

Photo-Oxidation of Bisphenol A in Aqueous Solutions at Near Neutral pH by a Fe(III)-Carboxylate Complex with Oxalacetic Acid as a Benign Molecule

The photo-oxidation of organic pollutants as induced by ferric-carboxylate complexes was known to be a photo-Fenton-like process. The use of a carboxylate ligand with higher efficiency and lower toxicity at near neutral pH is of high interest to researchers. In this work, photo-oxidation of bisphenol A (BPA) induced by a ferric-oxalacetic acid complex in aqueous solutions was investigated under 395 nm LED lamps. The results showed that the rate of BPA degradation increased in the order pH 10.0 << 8.0 < 6.5 < 4.0 within the first 10 min. More than 90% of BPA was successfully oxidized with Fe(III)/oxalacetic acid with a ratio of 1:5 at pH 6.5, which was primarily attributed to the generated hydroxyl radical. Iron in the Fe(III)-oxalacetic acid system was reused by simple addition of oxalacetic acid to the reaction mixture. Compared to common carboxylate ligands (pyruvic acid, oxalic acid, and citric acid), oxalacetic acid is more efficient and environmentally friendly for the Fe(III)-carboxylate complex-based photo-Fenton-like process at near neutral pH

Ultra-narrow band perfect absorbance induced by magnetic lattice resonances in dielectric dimer metamaterials

Nanostructured dielectric metamaterials have received extensive attention in the field of nanophotonics owing to their low radiative losses and coexisting electric and magnetic lattice resonance features. Unfortunately, suffering from the poor electromagnetic field localization and weak magnetic response in the typical dielectric metamaterials, it remains challenging to simultaneously realize ultra-narrow band perfect absorbance and intensified electromagnetic field resonances. Herein, we theoretically demonstrate a kind of dielectric metamaterials formed by dielectric cylindrical dimer array that supports magnetic lattice resonances. Benefiting from the collective diffraction coupling among the powerful magnetic dipole resonance in the dielectric dimer array, the proposed dielectric metamaterials synchronously manifest ultra-narrow spectral characteristics with bandwidth less than 8 nm, perfect absorbance amplitude as high as 99.7% and strong electric/magnetic field enhancement factor. The effects of the structure parameters on the optical properties of the proposed nanostructure are investigated based on numerical simulations. The linewidth of absorbance spectrum can be narrowed down to approximately 3 nm with optimal design. These excellent optical features supported by the dielectric dimer metamaterials can be explored as a high-efficiency refractive index sensor with sensitivity of 824 nm/RIU and figure of merit as high as 242 RIU−1. This work paves an exciting way for narrow band perfect absorbance and localized field enhancement, exhibiting tremendous enormous potential in biochemical sensing, surface enhanced spectroscopy, and nonlinear nanophotonics

Kramers–Kronig Transmission with a Crosstalk-Dependent Step Multiple-Input Multiple-Output Volterra Equalizer in a Seven-Core Fiber

In this paper, we experimentally demonstrate a net bit rate of 261.7 Gbit/s in a seven-core transmission system with a Kramers–Kronig (KK) receiver. The 10 GBaud 16-level quadrature amplitude modulation (QAM) signal is transmitted over a 2.5 km seven-core fiber, and the relationship between carrier-to-signal power ratio, signal power, frequency spacing, and optical power is analyzed. Moreover, a multiple-input multiple-output (MIMO) Volterra equalization algorithm with crosstalk-dependent steps is proposed to compensate for inter-core crosstalk and impairments induced by other devices. Compared to the single-input single-output (SISO) Volterra equalizer, the CSPR can be reduced by 1.3 dB, and the received power gain can reach up to 0.7 dB

Sugar Product Diversification and Its Opportunities in China

China is the third largest sugar producer in the world. However, the sugar industry is challenged by high production costs (attributed to the high cane supply price), which lead to continuous financial deficits for sugar companies. Currently, sugarcane products are primarily composed of white sugar and non-centrifugal sugar (NCS); the utilization of sugarcane by-products (i.e. bagasse, filter mud, and molasses) has also been partially industrialized. However, these strategies are far from adequate to address the challenge. China has successfully developed an innovative sugar manufacturing process using nano-ceramic membrane technology to overcome this predicament. In addition, concepts of high-value diversification of sugarcane products and value-added utilization of industrial byproducts have been proposed. A series of sugarcane-based products, e.g. NCS produced by nano-ceramic membrane technology (MNCS), sugarcane-based beverages, plant water, and cultivation substrates, have been developed to improve the overall return of sugar enterprises. Thus, this paper mainly summarizes the diversification of sugarcane products and their development opportunities in China

Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars

In steep wildfire-burned terrains, intense rainfall can produce large runoff that can trigger highly destructive debris flows. However, the ability to accurately characterize and forecast debris flow susceptibility in burned terrains using physics-based tools remains limited. Here, we augment the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) to simulate both overland and channelized flows and assess postfire debris flow susceptibility over a regional domain. We perform hindcast simulations using high-resolution weather-radar-derived precipitation and reanalysis data to drive non-burned baseline and burn scar sensitivity experiments. Our simulations focus on January 2021 when an atmospheric river triggered numerous debris flows within a wildfire burn scar in Big Sur-one of which destroyed California's famous Highway 1. Compared to the baseline, our burn scar simulation yields dramatic increases in total and peak discharge and shorter lags between rainfall onset and peak discharge, consistent with streamflow observations at nearby US Geological Survey (USGS) streamflow gage sites. For the 404 catchments located in the simulated burn scar area, median catchment-area-normalized peak discharge increases by ∼ 450% compared to the baseline. Catchments with anomalously high catchment-area-normalized peak discharge correspond well with post-event field-based and remotely sensed debris flow observations. We suggest that our regional postfire debris flow susceptibility analysis demonstrates WRF-Hydro as a compelling new physics-based tool whose utility could be further extended via coupling to sediment erosion and transport models and/or ensemble-based operational weather forecasts. Given the high-fidelity performance of our augmented version of WRF-Hydro, as well as its potential usage in probabilistic hazard forecasts, we argue for its continued development and application in postfire hydrologic and natural hazard assessments