Studies on the Property and Application of Starch Sugar Ester Dodecenylsuccinic

In this study, we have prepared starch and Brown algae sugar ester dodecenylsuccinic, and by using infrared rays, scanning electron microscopy (SEM), and differential scanning calorimetry (DSC), we studied the structures and properties of the starch and Brown algae sugar ester dodecenylsuccinic. In addition, we studied the possibility of using this modified starch and Brown algae as emulsifier that can be used in ice cream

Solvatochromic Parameters of the Binary Mixtures of Imidazolium Chloride Ionic Liquid Plus Molecular Solvent

Imidazolium-based chloride ionic liquids (ILs) have exhibited remarkable performance in several important applications such as biomass dissolution and extraction, but their large viscosity is a non-negligible problem. Adding molecular co-solvents into chloride ILs is effective in reducing viscosity; nevertheless, understanding of the accompanied change of thermodynamic polarity is quite few. Therefore, in this work we reported three Kamlet-Taft solvatochromic parameters, including dipolarity/polarizability π*), hydrogen-bond acidity (α) and hydrogen-bond basicity (β), for the binary mixtures of several imidazolium-based chloride ILs plus either dipolar protic solvents (water and methanol) or dipolar aprotic solvents (dimethyl sulfoxide, N,N-dimethylformamide and acetonitrile). The results demonstrated that those parameters could be altered by the structure of IL and type of co-solvent owing to the solute-solvent and solvent-solvent interactions. The structure of alkyl chain of cation had considerable impact on the π* variation of IL aqueous solution against IL concentration but hardly affected other mixtures. Moreover, remarkable preferential solvation of probes was observed for β and α in the mixtures of IL and dipolar aprotic co-solvents, whereas the hydrogen-bond interactions between IL and dipolar protic co-solvent enabled the preferential solvation to be alleviated and resulted in more linear variation of β and α against the molar fraction of IL. The results not only contribute to a better understanding of the effect of co-solvent on imidazolium-based chloride ILs, but also are instructive for improving the thermodynamic performance of IL-based applications via providing IL+co-solvent mixtures with desirable physicochemical properties

Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments

Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances

SEPT: Towards Scalable and Efficient Visual Pre-Training

Recently, the self-supervised pre-training paradigm has shown great potential in leveraging large-scale unlabeled data to improve downstream task performance. However, increasing the scale of unlabeled pre-training data in real-world scenarios requires prohibitive computational costs and faces the challenge of uncurated samples. To address these issues, we build a task-specific self-supervised pre-training framework from a data selection perspective based on a simple hypothesis that pre-training on the unlabeled samples with similar distribution to the target task can bring substantial performance gains. Buttressed by the hypothesis, we propose the first yet novel framework for Scalable and Efficient visual Pre-Training (SEPT) by introducing a retrieval pipeline for data selection. SEPT first leverage a self-supervised pre-trained model to extract the features of the entire unlabeled dataset for retrieval pipeline initialization. Then, for a specific target task, SEPT retrievals the most similar samples from the unlabeled dataset based on feature similarity for each target instance for pre-training. Finally, SEPT pre-trains the target model with the selected unlabeled samples in a self-supervised manner for target data finetuning. By decoupling the scale of pre-training and available upstream data for a target task, SEPT achieves high scalability of the upstream dataset and high efficiency of pre-training, resulting in high model architecture flexibility. Results on various downstream tasks demonstrate that SEPT can achieve competitive or even better performance compared with ImageNet pre-training while reducing the size of training samples by one magnitude without resorting to any extra annotations.Comment: Accepted by AAAI 202

Modifiable pathways for longevity:A Mendelian randomization analysis

Background: A variety of factors, including diet and lifestyle, obesity, physiology, metabolism, hormone levels, psychology, and inflammation, have been associated with longevity. The specific influences of these factors, however, are poorly understood. Here, possible causal relationships between putative modifiable risk factors and longevity are investigated. Methods: A random effects model was used to investigate the association between 25 putative risk factors and longevity. The study population comprised 11,262 long-lived subjects (≥90 years old, including 3484 individuals ≥99 years old) and 25,483 controls (≤60 years old), all of European ancestry. The data were obtained from the UK Biobank database. Genetic variations were used as instruments in two-sample Mendelian randomization to reduce bias. The odds ratios for genetically predicted SD unit increases were calculated for each putative risk factor. Egger regression was used to determine possible violations of the Mendelian randomization model. Results: Thirteen potential risk factors showed significant associations with longevity (≥90th) after correction for multiple testing. These included smoking initiation (OR:1.606; CI: 1.112–2.319) and educational attainment (OR:2.538, CI: 1.685–3.823) in the diet and lifestyle category, systolic and diastolic blood pressure (OR per SD increase: 0.518; CI: 0.438–0.614 for SBP and 0.620; CI 0.514–0.748 for DBP) and venous thromboembolism (OR:0.002; CI: 0.000–0.047) in the physiology category, obesity (OR: 0.874; CI: 0.796–0.960), BMI (OR per 1-SD increase: 0.691; CI: 0.628–0.760), and body size at age 10 (OR per 1-SD increase:0.728; CI: 0.595–0.890) in the obesity category, type 2 diabetes (T2D) (OR:0.854; CI: 0.816–0.894), LDL cholesterol (OR per 1-SD increase: 0.743; CI: 0.668–0.826), HDL cholesterol (OR per 1-SD increase: 1.243; CI: 1.112–1.390), total cholesterol (TC) (OR per 1-SD increase: 0.786; CI: 0.702–0.881), and triglycerides (TG) (OR per 1-SD increase: 0.865; CI: 0.749–0.998) in the metabolism category. Both longevity (≥90th) and super-longevity (≥99th), smoking initiation, body size at age 10, BMI, obesity, DBP, SBP, T2D, HDL, LDL, and TC were consistently associated with outcomes. The examination of underlying pathways found that BMI indirectly affected longevity through three pathways, namely, SBP, plasma lipids (HDL/TC/LDL), and T2D (p &lt; 0.05). Conclusion: BMI was found to significantly affect longevity through SBP, plasma lipid (HDL/TC/LDL), and T2D. Future strategies should focus on modifying BMI to improve health and longevity.</p

Effects of epigallocatechin-3-gallate on bovine oocytes matured

Objective Epigallocatechin-3-gallate (EGCG) is a major ingredient of catechin polyphenols and is considered one of the most promising bioactive compounds in green tea because of its strong antioxidant properties. However, the protective role of EGCG in bovine oocyte in vitro maturation (IVM) has not been investigated. Therefore, we aimed to study the effects of EGCG on IVM of bovine oocytes. Methods Bovine oocytes were treated with different concentrations of EGCG (0, 25, 50, 100, and 200 μM), and the nuclear and cytoplasmic maturation, cumulus cell expansion, intracellular reactive oxygen species (ROS) levels, total antioxidant capacity, the early apoptosis and the developmental competence of in vitro fertilized embryos were measured. The mRNA abundances of antioxidant genes (nuclear factor erythriod-2 related factor 2 [NRF2], superoxide dismutase 1 [SOD1], catalase [CAT], and glutathione peroxidase 4 [GPX4]) in matured bovine oocytes were also quantified. Results Nuclear maturation which is characterized by first polar body extrusion, and cytoplasmic maturation characterized by peripheral and cortical distribution of cortical granules and homogeneous mitochondrial distribution were significantly improved in the 50 μM EGCG-treated group compared with the control group. Adding 50 μM EGCG to the maturation medium significantly increased the cumulus cell expansion index and upregulated the mRNA levels of cumulus cell expansion-related genes (hyaluronan synthase 2, tumor necrosis factor alpha induced protein 6, pentraxin 3, and prostaglandin 2). Both the intracellular ROS level and the early apoptotic rate of matured oocytes were significantly decreased in the 50 μM EGCG group, and the total antioxidant ability was markedly enhanced. Additionally, both the cleavage and blastocyst rates were significantly higher in the 50 μM EGCG-treated oocytes after in vitro fertilization than in the control oocytes. The mRNA abundance of NRF2, SOD1, CAT, and GPX4 were significantly increased in the 50 μM EGCG-treated oocytes. Conclusion In conclusion, 50 μM EGCG can improve the bovine oocyte maturation, and the protective role of EGCG may be correlated with its antioxidative property

The genetic correlation and causal association between key factors that influence vascular calcification and cardiovascular disease incidence

Background: Serum calcium (Ca), vitamin D (VD), and vitamin K (VK) levels are key determinants of vascular calcification, which itself impacts cardiovascular disease (CVD) risk. The specific relationships between the levels of these different compounds and particular forms of CVD, however, remain to be fully defined. Objective: This study was designed to explore the associations between these serum levels and CVDs with the goal of identifying natural interventions capable of controlling vascular calcification and thereby protecting against CVD pathogenesis, extending the healthy lifespan of at-risk individuals.Methods: Linkage disequilibrium score (LDSC) regression and a two-sample Mendelian randomization (MR) framework were leveraged to systematically examine the causal interplay between these serum levels and nine forms of CVD, as well as longevity through the use of large publically accessible Genome-Wide Association Studies (GWAS) datasets. The optimal concentrations of serum Ca and VD to lower CVD risk were examined through a restrictive cubic spline (RCS) approach.Results: After Bonferroni correction, the positive genetic correlations were observed between serum Ca levels and myocardial infarction (MI) (p = 1.356E–04), as well as coronary artery disease (CAD) (p = 3.601E–04). Negative genetic correlations were detected between levels of VD and CAD (p = 0.035), while elevated VK1 concentrations were causally associated with heart failure (HF) [odds ratios (OR) per 1-standard deviation (SD) increase: 1.044], large artery stroke (LAS) (OR per 1-SD increase: 1.172), and all stroke (AS) (OR per 1-SD increase: 1.041). Higher serum Ca concentrations (OR per 1-SD increase: 0.865) and VD levels (OR per 1-SD increase: 0.777) were causally associated with reduced odds of longevity. These findings remained consistent in sensitivity analyses, and serum Ca and VD concentrations of 2.376 mmol/L and 46.8 nmol/L, respectively, were associated with a lower CVD risk (p &lt; 0.001). Conclusion: Our findings support a genetic correlation between serum Ca and VD and CVD risk, and a causal relationship between VK1 levels and CVD risk. The optimal serum Ca (2.376 mmol/L) and VD levels (46.8 nmol/L) can reduce cardiovascular risk.</p

The genetic correlation and causal association between key factors that influence vascular calcification and cardiovascular disease incidence

Background: Serum calcium (Ca), vitamin D (VD), and vitamin K (VK) levels are key determinants of vascular calcification, which itself impacts cardiovascular disease (CVD) risk. The specific relationships between the levels of these different compounds and particular forms of CVD, however, remain to be fully defined. Objective: This study was designed to explore the associations between these serum levels and CVDs with the goal of identifying natural interventions capable of controlling vascular calcification and thereby protecting against CVD pathogenesis, extending the healthy lifespan of at-risk individuals.Methods: Linkage disequilibrium score (LDSC) regression and a two-sample Mendelian randomization (MR) framework were leveraged to systematically examine the causal interplay between these serum levels and nine forms of CVD, as well as longevity through the use of large publically accessible Genome-Wide Association Studies (GWAS) datasets. The optimal concentrations of serum Ca and VD to lower CVD risk were examined through a restrictive cubic spline (RCS) approach.Results: After Bonferroni correction, the positive genetic correlations were observed between serum Ca levels and myocardial infarction (MI) (p = 1.356E–04), as well as coronary artery disease (CAD) (p = 3.601E–04). Negative genetic correlations were detected between levels of VD and CAD (p = 0.035), while elevated VK1 concentrations were causally associated with heart failure (HF) [odds ratios (OR) per 1-standard deviation (SD) increase: 1.044], large artery stroke (LAS) (OR per 1-SD increase: 1.172), and all stroke (AS) (OR per 1-SD increase: 1.041). Higher serum Ca concentrations (OR per 1-SD increase: 0.865) and VD levels (OR per 1-SD increase: 0.777) were causally associated with reduced odds of longevity. These findings remained consistent in sensitivity analyses, and serum Ca and VD concentrations of 2.376 mmol/L and 46.8 nmol/L, respectively, were associated with a lower CVD risk (p &lt; 0.001). Conclusion: Our findings support a genetic correlation between serum Ca and VD and CVD risk, and a causal relationship between VK1 levels and CVD risk. The optimal serum Ca (2.376 mmol/L) and VD levels (46.8 nmol/L) can reduce cardiovascular risk.</p