Curcumin Attenuates Retinal Vascular Leakage by Inhibiting Calcium/Calmodulin-Dependent Protein Kinase II Activity in Streptozotocin-Induced Diabetes

Background: Curcumin possesses many pharmacological properties including anti-inflammatory effects. Although prior studies indicate that curcumin has beneficial effects for diabetic retinopathy, the mechanism of action is not known. To address this issue, we investigated the effect of curcumin against diabetes-induced retinal vascular damage and its mechanism of action by using cultured retinal Müller cells stimulated with high glucose. Methods: We studied the effects of curcumin in vivo in the retinas of rats rendered diabetic by streptozotocin and in vitro in Müller cells stimulated with high glucose. We administered curcumin, or KN93, an inhibitor of calcium/calmodulin dependent protein kinase II (CaMKII), or saline vehicle to experimental animals on a daily basis for 12 weeks. Primary cultures of rat Müller cells were incubated with normal glucose or high glucose, with or without curcumin, KN93, or pyrrolidine dithiocarbamate (PDTC), an inhibitor of the transcription protein nuclear factor κB (NF-κB). We examined mRNA and protein levels of vascular endothelial growth factor (VEGF), inducible nitric oxide synthase (iNOS) and intercellular adhesion molecule-1 (ICAM-1) by real-time RT-PCR and Western blotting, respectively. Retinal levels of CaMKII and NF-κB were examined by Western blotting. Vascular leakage was evaluated using Evans blue. Results: Curcumin and KN93 significantly inhibited the activation of CaMKII/NF-κB signaling induced by diabetes or elevated glucose, and subsequently decreased the expression of VEGF, iNOS and ICAM-1. These changes were associated with a decrease of diabetes-induced retinal vascular leakage. Conclusion: Curcumin protects the diabetic rat retina against early retinal vascular damage, by inhibition of CaMKII activity. Curcumin is currently used to treat a number of clinical conditions, and may prove beneficial for the management of diabetic retinopathy

CO2 emission from container glass in China, and emission reduction strategy analysis

Glass is one of the materials most extensively used for packaging. Its manufacturing process is typically energy intensive, with a large quantity of CO2 emissions. In this study, the material and energy consumptioninventory of the manufacturing stage were quantified and CO2 emissions from the glass manufacturing process were calculated. When fuel oil, coal or natural gas is used as the major energy source in the production system, it produces CO2 emissions of 1.2798, 0.6250 and 0.4498 t/t, respectively. CO2 emissions from fossil fuel combustion account for 67.79% of the total emissions and a huge potential exists for emission reduction. Due to differences in energy structure, current research results cannot represent the emissions in China; therefore, six scenarios were created to explore the CO2 emission reduction potential of the glass industry with different energy structures, and six scenarios were created to explore the cost from the adjustment of the energy structure. According to the analysis of the glass furnace, the effective heat accounts for 35.31%, so the basic approach for energy saving and emission reduction is to enhance the effective heat quantity of the glass furnace. Also, energy and virgin feedstock savings were calculated for 1 t of glass container production with different recycling levels

Mesoporous KIT-6 Supported Pd-MxOy (M = Ni, Co, Fe) Catalysts with Enhanced Selectivity for p-Chloronitrobenzene Hydrogenation

Bimetallic PdM (M = Ni, Co, Fe) nanoparticles were synthesized using butyllithium as a reductant, and were used to prepare Pd-M (x) O (y) /KIT-6 catalysts by appropriate oxidation and reduction treatments. These catalysts showed higher selectivity in the hydrogenation of p-chloronitrobenzene to p-chloroaniline than Pd/KIT-6. Relevant characterization was conducted using X-ray diffraction, transmission electron microscopy, N-2 adsorption-desorption, X-ray photoelectron spectroscopy, inductive coupled plasma emission spectrometer, and H-2 temperature-programmed reduction

Implementation of stimuli with millisecond timing accuracy in online experiments.

Online experiments are growing in popularity. This study aimed to determine the timing accuracy of web technologies and investigate whether they can be used to support high temporal precision psychology experiments. A dynamic sinusoidal grating and flashes were produced by setInterval, CSS3, and requestAnimationFrame (hereafter, rAF) technologies. They were run at normal or real-time priority processing in Chrome, Firefox, Edge, and Internet Explorer on Windows, macOS, and Linux. Timing accuracies were compared with that of Psychtoolbox which was chosen as gold standard. It was found that rAF with real-time priority had the best timing accuracy compared to the other web technologies and had a similar timing accuracy as Psychtoolbox in traditional experiments in most cases. However, rAF exhibited poor timing accuracy on Linux. Therefore, rAF can be used as technical basis for accuracy of millisecond timing sequences in online experiments, thereby benefiting the psychology field

Nrat: towards adversarial training with inherent label noise

AbstractAdversarial training (AT) has been widely recognized as the most effective defense approach against adversarial attacks on deep neural networks and it is formulated as a min-max optimization. Most AT algorithms are geared towards research-oriented datasets such as MNIST, CIFAR10, etc., where the labels are generally correct. However, noisy labels, e.g., mislabelling, are inevitable in real-world datasets. In this paper, we investigate AT with inherent label noise, where the training dataset itself contains mislabeled samples. We first empirically show that the performance of AT typically degrades as the label noise rate increases. Then, we propose a Noisy-Robust Adversarial Training (NRAT) algorithm, which leverages the recent advancements in learning with noisy labels to enhance the performance of AT in the presence of label noise. For experimental comparison, we consider two essential metrics in AT: (i) trade-off between natural and robust accuracy; (ii) robust overfitting. Our experiments show that NRAT’s performance is on par with, or better than, the state-of-the-art AT methods on both evaluation metrics. Our code is publicly available at: https://github.com/TrustAI/NRAT.</jats:p

Factors affecting the accuracy of genomic prediction in joint pig populations

Genomic prediction (GP) has greatly advanced animal and plant breeding over the past two decades. GP in joint populations is a feasible method to improve the accuracy of genomic estimated breeding values in small populations. However, there is still a need to understand the factors that influence GP in joint populations. This study used simulated data and real data from Duroc pig populations to examine the impact of linkage disequilibrium (LD), causal variants effect sizes (CVESs), and minor allele frequencies (MAF) of SNPs on the accuracy of genomic prediction in joint populations. Three prediction methods were used: genomic best linear unbiased prediction (GBLUP), single-step GBLUP and multi-trait GBLUP. Results from the simulated datasets showed that the accuracies of GP in joint populations were always higher than those in a single population when only LD inconsistencies existed. However, single-step GBLUP accuracy in joint populations decreased as the correlation of MAF between populations decreased, while the accuracy of GBLUP is consistently higher in joint populations than in a single population. As the correlation of CVES between populations decreased, the accuracy of both GBLUP and single-step GBLUP in joint populations declined. Analysis of real Duroc populations showed low genetic correlation, similar to the simulated relationship between the most distant populations. In most cases in Duroc populations, GP have higher accuracies in joint populations than in individual population. In conclusion, the consistency of CVES plays a more important role in multi-population GP. The genetic relatedness of the Duroc populations is so weak that the prediction accuracy of GP in joint populations is reduced in some traits. Multi-trait GBLUP is a competitive method for the joint breeding evaluation

Effects of elevated carbon dioxide on metal transport in soil-crop system: results from a field rice and wheat experiment

Purpose Climate changes have significant impacts on crop yield, and also on crop quality related to food safety and human health. This study investigated the influences of atmospheric carbon dioxide (CO2) enrichment on cereal metal accumulation in soil-crop system. Materials and methods Field rotation experiments of rice (Oryza sativa) and winter wheat (Triticum aestivum) were conducted by simulating elevated CO2 concentrations (e[CO2]) in 12 open-top chambers (OTCs). The treatments included the ambient [CO2] (CK), 80 ppm (T1) and 200 ppm (T2) above ambient condition, respectively. Different parts of above-ground plant samples were analyzed for metal concentrations (Cu, Zn, Fe, Mn; Ca, Mg) at the key growth stages, assisted with analyses of soil pH, metal bioavailability, and transfer factors (TFs). Results and discussion The result patterns were opposite for rice and wheat. After the increased transport from rhizospheric soil solution due to the metal mobilization by declined pH, most metals increased their distributions in rice grain, husk, and stem than leaf. While for winter wheat, though soil metal bioavailability was also increased, their distributions in grain, husk, and stem were decreased owing to possible carbohydrate dilution effect or cation competition, except some macro metals distributed more in leaf. Conclusions Since results of metals and crops are not always consistent among various reports, the mechanisms of essential/toxic metal transport in soil-crop system affected by climate change and its impacts on human health deserve further studies