Therapeutic Effects of Glutamic Acid in Piglets Challenged with Deoxynivalenol

The mycotoxin deoxynivalenol (DON), one of the most common food contaminants, primarily targets the gastrointestinal tract to affect animal and human health. This study was conducted to examine the protective function of glutamic acid on intestinal injury and oxidative stress caused by DON in piglets. Twenty-eight piglets were assigned randomly into 4 dietary treatments (7 pigs/treatment): 1) uncontaminated control diet (NC), 2) NC+DON at 4 mg/kg (DON), 3) NC+2% glutamic acid (GLU), and 4) NC+2% glutamic acid + DON at 4 mg/kg (DG). At day 15, 30 and 37, blood samples were collected to determine serum concentrations of CAT (catalase), T-AOC (total antioxidant capacity), H2O2 (hydrogen peroxide), NO (nitric oxide), MDA (maleic dialdehyde), DAO (diamine oxidase) and D-lactate. Intestinal morphology, and the activation of Akt/mTOR/4EBP1 signal pathway, as well as the concentrations of H2O2, MDA, and DAO in kidney, liver and small intestine, were analyzed at day 37. Results showed that DON significantly (P<0.05) induced oxidative stress in piglets, while this stress was remarkably reduced with glutamic acid supplementation according to the change of oxidative parameters in blood and tissues. Meanwhile, DON caused obvious intestinal injury from microscopic observations and permeability indicators, which was alleviated by glutamic acid supplementation. Moreover, the inhibition of DON on Akt/mTOR/4EBP1 signal pathway was reduced by glutamic acid supplementation. Collectively, these data suggest that glutamic acid may be a useful nutritional regulator for DON-induced damage manifested as oxidative stress, intestinal injury and signaling inhibition

Machine-learning detector based on support vector machine for 122-Gbps multi-CAP optical communication system

In this work, we firstly apply support vector machine (SVM) detector in 122-Gbps Multi-CAP system. It can de-map the rotated constellations directly without any correction. The concrete simulations indicate such a machine-learning based detector provides considerable BER reduction for high-density CAPs in low-frequency band, compared with hard decision

Nonlinear distortion mitigation by machine learning of SVM classification for PAM-4 and PAM-8 modulated optical interconnection

We demonstrated a support vector machine (SVM) based machine learning method to mitigate modulation nonlinearity distortion for PAM-4 and PAM-8 vertical cavity surface emitter laser multi-mode fiber (VCSEL-MMF) optical link. Simulations at 100 Gb/s data rate and experimental work at 60 Gb/s data rate were carried out. We achieved a significant improvement in bit error rate (BER) when complete binary tree SVMs (CBT-SVMs) are applied for both PAM-4 and PAM-8 signals. Quantitative analysis of the sensitivity gain versus modulation nonlinearity distortion is presented with experimentally verification. The results indicate that CBT-SVMs have better performance for PAM-8 compared to PAM-4. The sensitivity gain increases almost linearly with the increase of eye-linearity (increase of modulation nonlinearity distortion). Up to 2.5-dB sensitivity improvement is achieved by the proposed CBT-SVMs at eye-linearity of 1.72 for PAM-4.</p

Machine learning adaptive receiver for PAM-4 modulated optical interconnection based on silicon microring modulator

Modulation nonlinearity can severely distort multi-level modulation, and signal processing to mitigate the distortion is highly desirable. In this work, we demonstrated a machine learning method for adaptive detection of 4-level pulse amplitude modulation (PAM-4) signals modulated by silicon micro-ring modulator (Si-MRM). The very limited linear modulation range of Si-MRM leads to serious modulation nonlinearity distortion for high-level modulations like PAM-4 with the consideration of wavelength drift. Our approach is based on the support vector machine (SVM) method which can learn the nonlinear distortion of Si-MRM during PAM-4 modulation. Thus, the detection can be made adaptive for PAM-4 signals with nonlinear levels and level dependent noise. The modulation nonlinearity distortion of PAM-4 has been characterized in terms of level deviation (LD) with respect to wavelength drift. Up to 2.7-dB receiver sensitivity gain is obtained at about 26% LD by using the proposed SVM machine learning method. The receiver sensitivity-float range can be squeezed to be within 0.3 dB even with up to 30% LD which indicates a stable detection of PAM-4 signals along with wavelength drift. Up to 3.63-dB receiver sensitivity improvement has been experimentally achieved at 50 Gbps for PAM-4 signals modulated by a Si-MRM and after 2-km standard single mode fiber (SSMF) transmission. The stable operation of Si-MRM is very difficult and very important. The proof-of-concept results indicate the very promising capability of machine learning method for stable detection of PAM-4 signals modulated by Si-MRM, which is of great significance for practical application of Si-MRM in optical interconnection.</p

Liver and kidney anti-oxidative capacity in each group.

<p>A: MDA concentrations of liver and kidney in each group. B: H₂O₂ concentrations of liver and kidney in each group. C: T-AOC of liver and kidney in each group. Dietary treatments were NC, an uncontaminated basal diet, DON, the basal contaminated with 4 mg/kg deoxynivalenol, GLU, uncontaminated basal diet with 2% (g/g) glutamic acid supplementation, and DG, deoxynivalenol-contaminated (4 mg/kg) basal diet with 2% (g/g) glutamic acid supplementation. Data are presented as means ± SEM, n = 7, with a–b used to indicate a statistically significant difference (P<0.05, one way ANOVA method). MDA: methane dicarboxylic aldehyde (nmol/ml); H₂O₂: hydrogen peroxide (mmol/L); T-AOC: total antioxidant capacity (U/mg).</p

Image of ileal goblet cells and ileal lymphocytes.

<p>A: Ileal goblet cells (×250). B: Ileam lymphocytes (×400). Dietary treatments were NC, an uncontaminated basal diet, DON, the basal contaminated with 4 mg/kg deoxynivalenol, GLU, uncontaminated basal diet with 2% (g/g) glutamic acid supplementation, and DG, deoxynivalenol-contaminated (4 mg/kg) basal diet with 2% (g/g) glutamic acid supplementation. n = 7 for treatments.</p

Effect of dietary supplementation with glutmic acid on serum amino acids levels (µg/ml) in piglets fed a deoxynivalenol-contaminated diet on day 15.

<p>NC = uncontaminated basal diet, DON = basal diet contaminated with deoxynivalenol (4 mg/kg), GLU = uncontaminated basal diet supplemented with 2% glutamic acid.; DG = DON diet supplemented with 2% glutamic acid. Data are presented as means ± SEM, n = 7, with a–b used to indicate a statistically significant difference (P<0.05, one way ANOVA method).</p