Relationship of Total Phenolic Contents, DPPH Activities and Anti-Lipid-Oxidation Capabilities of Different Bioactive Beverages and Phenolic Antioxidants

Polyphenol compounds are widely used as antioxidants in food processing. The conventional phenolic content, scavenging DPPH free radical assays have been widely used for antioxidant evaluation. This research aimed to study the correlation between scavenging DPPH (2, 2-diphenyl-1-picrylhydrazyl) free radical activity, total phenolic content and anti-lipid-oxidation capability of natural and synthetic antioxidants and four beverages drinks. Synthetic phenol antioxidants, BHA (butylated hydroxyanisole), BHT (butylated hydroxytoluene), PG (propyl gallate), TBHQ (tert-butylhydroquinone) and naturally occurring phenolics ƒÑ-T(£\-tocopherol), CA(Chlorogenic acid), GA(Gallic acid), QCT(Quercetin), RSV(Resveratrol), and SA(Syringic acid) were examined for their total phenolic content (TPC) and scavenging DPPH free radical activity. The anti-lipid-oxidation capability of these antioxidants was determined by using a fish oil emulsion which imitated the blood serum environment. CA and GA had great phenolic content, while they were in the group of lowest anti-lipid-oxidation capability. Oppositely, BHA was in the lowest group of phenolic content but provided the greatest anti-lipid-oxidation capability. The DPPH annihilation activity of GA was equivalent to PG and QCT and among the top three performances in the assay. However, its anti-lipid-oxidation capability was four times lower than PG or QCT. In general, most of the synthetic antioxidants had poor phenolic content but demonstrated better anti-lipid-oxidation capability, while, the phenolic acids such as GA and CA, were totally reverse. Only QCT and PG exhibited great performance in all the three assays. Thus, the fish oil emulsion developed in this study could be an efficient and reliable model for the evaluation of various antioxidants which overcomes the unilateral reaction of conventional spectrophotometric assays. Tea, coffee, red wine and white wine were also investigated to assess the correlations of TPC, DPPH free radical scavenging activity and anti-lipid-oxidation capability in a fish oil emulsion model. Diversities and concentrations of the major phenolics in the four beverages were also determined. With the most abundant phenolics, red wine dominated in TPC, DPPH scavenging activity, and anti-lipid-oxidation capability. However, white wine which had better performance in the TPC and DPPH assays showed the lowest capability in preventing fish lipid oxidation. The anti-lipid-oxidation capability of tea or coffee was much higher than white wine, although the three beverages had similar TPC. Therefore, the results from TPC and DPPH assays may not always correspond to the actual anti-lipid-oxidation capabilities. As the fish oil emulsion model was designed to imitate the human serum, the obtained anti-lipid-oxidation capability could closely reflect the antioxidant activity in stabilizing lipids and reducing harmful lipid oxidation products in the serum

Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1

Global Navigation Satellite System (GNSS) signals can be exploited to remotely sense atmosphere and land and ocean surface to retrieve a range of geophysical parameters. This paper proposes two new methods, termed as power-summation of differential Delay-Doppler Maps (PS-D) and pixel-number of differential Delay-Doppler Maps (PN-D), to distinguish between sea ice and sea water using differential Delay-Doppler Maps (dDDMs). PS-D and PN-D make use of power-summation and pixel-number of dDDMs, respectively, to measure the degree of difference between two DDMs so as to determine the transition state (water-water, water-ice, ice-ice and ice-water) and hence ice and water are detected. Moreover, an adaptive incoherent averaging of DDMs is employed to improve the computational efficiency. A large number of DDMs recorded by UK TechDemoSat-1 (TDS-1) over the Arctic region are used to test the proposed sea ice detection methods. Through evaluating against ground-truth measurements from the Ocean Sea Ice SAF, the proposed PS-D and PN-D methods achieve a probability of detection of 99.72% and 99.69% respectively, while the probability of false detection is 0.28% and 0.31% respectively

Enhancing Traffic Prediction with Learnable Filter Module

Modeling future traffic conditions often relies heavily on complex spatial-temporal neural networks to capture spatial and temporal correlations, which can overlook the inherent noise in the data. This noise, often manifesting as unexpected short-term peaks or drops in traffic observation, is typically caused by traffic accidents or inherent sensor vibration. In practice, such noise can be challenging to model due to its stochastic nature and can lead to overfitting risks if a neural network is designed to learn this behavior. To address this issue, we propose a learnable filter module to filter out noise in traffic data adaptively. This module leverages the Fourier transform to convert the data to the frequency domain, where noise is filtered based on its pattern. The denoised data is then recovered to the time domain using the inverse Fourier transform. Our approach focuses on enhancing the quality of the input data for traffic prediction models, which is a critical yet often overlooked aspect in the field. We demonstrate that the proposed module is lightweight, easy to integrate with existing models, and can significantly improve traffic prediction performance. Furthermore, we validate our approach with extensive experimental results on real-world datasets, showing that it effectively mitigates noise and enhances prediction accuracy

NF-Y Recruits Both Transcription Activator and Repressor to Modulate Tissue- and Developmental Stage-Specific Expression of Human γ-Globin Gene

The human embryonic, fetal and adult β-like globin genes provide a paradigm for tissue- and developmental stage-specific gene regulation. The fetal γ-globin gene is expressed in fetal erythroid cells but is repressed in adult erythroid cells. The molecular mechanism underlying this transcriptional switch during erythroid development is not completely understood. Here, we used a combination of in vitro and in vivo assays to dissect the molecular assemblies of the active and the repressed proximal γ-globin promoter complexes in K562 human erythroleukemia cell line and primary human fetal and adult erythroid cells. We found that the proximal γ-globin promoter complex is assembled by a developmentally regulated, general transcription activator NF-Y bound strongly at the tandem CCAAT motifs near the TATA box. NF-Y recruits to neighboring DNA motifs the developmentally regulated, erythroid transcription activator GATA-2 and general repressor BCL11A, which in turn recruit erythroid repressor GATA-1 and general repressor COUP-TFII to form respectively the NF-Y/GATA-2 transcription activator hub and the BCL11A/COUP-TFII/GATA-1 transcription repressor hub. Both the activator and the repressor hubs are present in both the active and the repressed γ-globin promoter complexes in fetal and adult erythroid cells. Through changes in their levels and respective interactions with the co-activators and co-repressors during erythroid development, the activator and the repressor hubs modulate erythroid- and developmental stage-specific transcription of γ-globin gene.</p

DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model

Pervasive integration of GPS-enabled devices and data acquisition technologies has led to an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal data mining research. Nonetheless, GPS trajectories contain personal geolocation information, rendering serious privacy concerns when working with raw data. A promising approach to address this issue is trajectory generation, which involves replacing original data with generated, privacy-free alternatives. Despite the potential of trajectory generation, the complex nature of human behavior and its inherent stochastic characteristics pose challenges in generating high-quality trajectories. In this work, we propose a spatial-temporal diffusion probabilistic model for trajectory generation (DiffTraj). This model effectively combines the generative abilities of diffusion models with the spatial-temporal features derived from real trajectories. The core idea is to reconstruct and synthesize geographic trajectories from white noise through a reverse trajectory denoising process. Furthermore, we propose a Trajectory UNet (Traj-UNet) deep neural network to embed conditional information and accurately estimate noise levels during the reverse process. Experiments on two real-world datasets show that DiffTraj can be intuitively applied to generate high-fidelity trajectories while retaining the original distributions. Moreover, the generated results can support downstream trajectory analysis tasks and significantly outperform other methods in terms of geo-distribution evaluations

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: RNDVI = (1 + e-15.2829x(RAGDDi-0.1944))-1 - (1 + e-11.6517x(RAGDDi-1.0267))-1 (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status