Aging plays a crucial role in the mechanisms of the impacts of genetic and environmental factors on blood pressure and serum lipids. However, to our knowledge, how the influence of genetic and environmental factors on the correlation between blood pressure and serum lipids changes with age remains to be determined. In this study, data from the Chinese National Twin Registry (CNTR) were used. Resting blood pressure, including systolic and diastolic blood pressure (SBP and DBP), and fasting serum lipids, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglycerides (TGs) were measured in 2378 participants (1189 twin pairs). Univariate and bivariate structural equation models examined the genetic and environmental influences on blood pressure and serum lipids among three age groups. All phenotypes showed moderate to high heritability (0.37–0.59) and moderate unique environmental variance (0.30–0.44). The heritability of all phenotypes showed a decreasing trend with age. Among all phenotypes, SBP and DBP showed a significant monotonic decreasing trend. For phenotype-phenotype pairs, the phenotypic correlation (Rph) of each pair ranged from −0.04 to 0.23, and the additive genetic correlation (Ra) ranged from 0.00 to 0.36. For TC&SBP, TC&DBP, TG&SBP and TGs&DBP, both the Rph and Ra declined with age, and the Ra difference between the young group and the older adult group is statistically significant (p < .05). The unique environmental correlation (Re) of each pair did not follow any pattern with age and remained relatively stable with age. In summary, we observed that the heritability of blood pressure was affected by age. Moreover, blood pressure and serum lipids shared common genetic backgrounds, and age had an impact on the phenotypic correlation and genetic correlations.

The effects of monolaurin (ML) on the health of piglets infected with porcine epidemic diarrhoea virus (PEDV) have not been fully understood. This study aimed to investigate its role in blood biochemical profile, intestinal barrier function, antioxidant function and the expression of antiviral genes in piglets infected with PEDV. Thirty-two piglets were randomly divided into four groups: control group, ML group, PEDV group and ML + PEDV group. Piglets were orally administrated with ML at a dose of 100 mg/kg·BW for 7 d before PEDV infection. Results showed that PEDV infection significantly decreased D-xylose content and increased intestinal fatty acid-binding protein content, indicating that PEDV infection destroyed intestinal barrier and absorption function. While it could be repaired by ML administration. Moreover, ML administration significantly decreased plasma blood urea nitrogen and total protein content upon PEDV infection. These results suggested ML may increase protein utilisation efficiency. ML administration significantly decreased the number of large unstained cells and Hb and increased the number of leucocytes and eosinophils in the blood of PEDV-infected piglets, indicating ML could improve the immune defense function of the body. In the presence of PEDV infection, ML administration significantly increased superoxide dismutase and catalase activities in blood and colon, respectively, indicating ML could improve antioxidant capacity. Besides, ML administration reversed the expression of ISG15, IFIT3 and IL-29 throughout the small intestine and Mx1 in jejunum and ileum, indicating the body was in recovery from PEDV infection. This study suggests that ML could be used as a kind of feed additive to promote swine health upon PEDV infection.

The advent of time-domain sky surveys has generated a vast amount of light variation data, enabling astronomers to investigate variable stars with large-scale samples. However, this also poses new opportunities and challenges for the time-domain research. In this paper, we focus on the classification of variable stars from the Catalina Surveys Data Release 2 and propose an imbalanced learning classifier based on Self-paced Ensemble (SPE) method. Compared with the work of Hosenie et al. (2020), our approach significantly enhances the classification Recall of Blazhko RR Lyrae stars from 12% to 85%, mixed-mode RR Lyrae variables from 29% to 64%, detached binaries from 68% to 97%, and LPV from 87% to 99%. SPE demonstrates a rather good performance on most of the variable classes except RRab, RRc, and contact and semi-detached binary. Moreover, the results suggest that SPE tends to target the minority classes of objects, while Random Forest is more effective in finding the majority classes. To balance the overall classification accuracy, we construct a Voting Classifier that combines the strengths of SPE and Random Forest. The results show that the Voting Classifier can achieve a balanced performance across all classes with minimal loss of accuracy. In summary, the SPE algorithm and Voting Classifier are superior to traditional machine learning methods and can be well applied to classify the periodic variable stars. This paper contributes to the current research on imbalanced learning in astronomy and can also be extended to the time-domain data of other larger sky survey projects (LSST, etc.).

An all-fiber high-power linearly polarized chirped pulse amplification (CPA) system is experimentally demonstrated. Through stretching the pulse duration to a full width of approximately 2 ns with two cascaded chirped fiber Bragg gratings (CFBGs), a maximum average output power of 612 W is achieved from a high-gain Yb-doped fiber that has a core diameter of 20 μm with a slope efficiency of approximately 68% at the repetition rate of 80 MHz. At the maximum output power, the polarization degree is 92.5% and the M2 factor of the output beam quality is approximately 1.29; the slight performance degradations are attributed to the thermal effects in the main amplifier. By optimizing the B-integral of the amplifier and finely adjusting the higher-order dispersion of one of the CFBGs, the pulse width is compressed to 863 fs at the highest power with a compression efficiency of 72%, corresponding to a maximum compressed average power of 440.6 W, single pulse energy of 5.5 μJ and peak power of about 4.67 MW. To the best of our knowledge, this is the highest average power of a femtosecond laser directly generated from an all-fiber linearly polarized CPA system.

It is crucial to understand the genetic mechanisms and biological pathways underlying the relationship between obesity and serum lipid levels. Structural equation models (SEMs) were constructed to calculate heritability for body mass index (BMI), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and the genetic connections between BMI and the four classes of lipids using 1197 pairs of twins from the Chinese National Twin Registry (CNTR). Bivariate genomewide association studies (GWAS) were performed to identify genetic variants associated with BMI and lipids using the records of 457 individuals, and the results were further validated in 289 individuals. The genetic background affecting BMI may differ by gender, and the heritability of males and females was 71% (95% CI [.66, .75]) and 39% (95% CI [.15, .71]) respectively. BMI was positively correlated with TC, TG and LDL-C in phenotypic and genetic correlation, while negatively correlated with HDL-C. There were gender differences in the correlation between BMI and lipids. Bivariate GWAS analysis and validation stage found 7 genes (LOC105378740, LINC02506, CSMD1, MELK, FAM81A, ERAL1 and MIR144) that were possibly related to BMI and lipid levels. The significant biological pathways were the regulation of cholesterol reverse transport and the regulation of high-density lipoprotein particle clearance (p < .001). BMI and blood lipid levels were affected by genetic factors, and they were genetically correlated. There might be gender differences in their genetic correlation. Bivariate GWAS analysis found MIR144 gene and its related biological pathways may influence obesity and lipid levels.

According to Hamilton's rule, matrilineal-biased investment restrains men in matrilineal societies from maximising their inclusive fitness (the ‘matrilineal puzzle'). A recent hypothesis argues that when women breed communally and share household resources, a man should help his sisters' household, rather than his wife's household, as investment to the later but not the former would be diluted by other unrelated members (Wu et al., 2013). According to this hypothesis, a man is less likely to help on his wife's farm when there are more women reproducing in the wife's household, because on average he would be less related to his wife's household. We used a farm-work observational dataset, that we collected in the matrilineal Mosuo in southwest China, to test this hypothesis. As predicted, high levels of communal breeding by women in his wife's households do predict less effort spent by men on their wife's farm, and communal breeding in men's natal households do not affect whether men help on their natal farms. Thus, communal breeding by women dilutes the inclusive fitness benefits men receive from investment to their wife and children, and may drive the evolution of matrilineal-biased investment by men. These results can help solve the ‘matrilineal puzzle'.