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In this study, we conducted indoor temporary plugging and diverting fracturing (TPDF) experiments on samples of natural deep and shallow coal seams using a small-scale true triaxial fracturing simulation system. By integrating high-precision CT scanning technology and crack reconstruction techniques, and relying on the quantitative characterization of the crack complexity coefficient, we investigated the differences in internal structures between deep and shallow coal seam samples and the variations in the expansion of TPDF cracks. Furthermore, the study primarily focused on exploring the influence of temporary plugging agent (TPA) parameters (quantity and particle size) on the expansion patterns of TPDF cracks in deep coal seam coal-rock samples. The experimental results reveal that in shallow coal samples, artificially induced fractures are notably longer and extend to various surfaces of the samples. Conversely, in deep coal samples, the expansion of artificially induced fractures is influenced by well-developed cleavage and cleats. The crack complexity coefficient of artificial fractures in deep coal samples is 1.54 higher than that in shallow coal samples, with an increased crack width of 98 μm. However, the expansion distance of the fractures is shorter and does not extend to the S3 and S6 surfaces. Increasing the dosage of the TPA is advantageous for inducing frequent redirection of fractures and communication with previously untouched areas. When the dosage of the TPA is increased from 30 g/L to 60 g/L, the expansion distance of artificial fractures significantly increases, extending to various surfaces of the samples. The crack complexity coefficient increases by 1.1, and the crack width enlarges by 84 μm. Appropriate particle sizes of the TPA can effectively form seals. However, small particle sizes (200/400 mesh) struggle to seal initial fractures, while large particle sizes (10/20 mesh) can lead to excessive wellhead blockage, causing rapid shut-in pressure, destructive fracturing in deep coal samples, and the generation of a large amount of coal fines. This study holds significant guidance for the optimal selection of process parameters in the temporary plugging hydraulic fracturing of deep coal rock.

Optical polarization has attracted considerable research attention by extra detection dimension in angular space, flourishing modern optoelectronic applications. Nonetheless, purposive polarization controlling at nanoscales and even at the single-particle level constitutes a challenge. Plasmonic nanoantenna opens up new perspectives in polarization state modification. Herein, we report angular-dependent upconversion luminescence (UCL) of rare-earth ions doped upconversion nanoparticles (UCNPs) in both emission and excitation polarization via constructing angularly anisotropic plasmonic local-field distributions in gold nanorods (Au NRs) antennae with different configurations at a single-particle level. The UCL of UCNP tailored by plasmonic Au NRs nanoantennae is enhanced and exhibits linear polarization. The highest enhancement factor of 138 is obtained in the collinear Au NR-UCNP-Au NR configuration under parallel excitation. Simultaneously, the maximum degree of linear polarization (DOLP) of UCL with factors of 85% and 81% are achieved in the same structure in emission and excitation polarization measurements, respectively. The observed linear polarizations and UCL enhancements are due to varied resonant responses at 660 nm and the anisotropic near-field enhancement in different nanoantennae-load UCNP. The theoretical simulations reveal the periodic changing of near-field enhancement factors of nanoantennae in angular space with the incident light polarization angles and are well-matched with the experimental results.

Understanding the relationship between individuals social networks and health could help devise public health interventions for reducing incidence of unhealthy behaviors or increasing prevalence of healthy ones. In this context, we explore the co-evolution of individuals social network positions and physical activities. We are able to do so because the NetHealth study at the University of Notre Dame has generated both high-resolution longitudinal social network (e.g., SMS) data and high-resolution longitudinal health-related behavioral (e.g., Fitbit physical activity) data. We examine trait differences between (i) users whose social network positions (i.e., centralities) change over time versus those whose centralities remain stable, (ii) users whose Fitbit physical activities change over time versus those whose physical activities remain stable, and (iii) users whose centralities and their physical activities co-evolve, i.e., correlate with each other over time. We find that centralities of a majority of all nodes change with time. These users do not show any trait difference compared to time-stable users. However, if out of all users whose centralities change with time we focus on those whose physical activities also change with time, then the resulting users are more likely to be introverted than time-stable users. Moreover, users whose centralities and physical activities both change with time and whose evolving centralities are significantly correlated (i.e., co-evolve) with evolving physical activities are more likely to be introverted as well as anxious compared to those users who are time-stable and do not have a co-evolution relationship. Our network analysis framework reveals several links between individuals social network structure, health-related behaviors, and the other (e.g., personality) traits. In the future, our study could lead to development of a predictive model of social network structure from behavioral/trait information and vice versa.

BACKGROUND. Low vitamin D status in pregnancy was proposed as a risk factor of preeclampsia. METHODS. We assessed the effect of vitamin D supplementation (4,400 vs. 400 IU/day), initiated early in pregnancy (10-18 weeks), on the development of preeclampsia. The effects of serum vitamin D (25-hydroxyvitamin D [25OHD]) levels on preeclampsia incidence at trial entry and in the third trimester (32-38 weeks) were studied. We also conducted a nested case-control study of 157 women to investigate peripheral blood vitamin D-associated gene expression profiles at 10 to 18 weeks in 47 participants who developed preeclampsia. RESULTS. Of 881 women randomized, outcome data were available for 816, with 67 (8.2%) developing preeclampsia. There was no significant difference between treatment (N = 408) or control (N = 408) groups in the incidence of preeclampsia (8.08% vs. 8.33%, respectively; relative risk: 0.97; 95% CI, 0.61-1.53). However, in a cohort analysis and after adjustment for confounders, a significant effect of sufficient vitamin D status (25OHD ≥ 30 ng/ml) was observed in both early and late pregnancy compared with insufficient levels (25OHD CONCLUSIONS. Vitamin D supplementation initiated in weeks 10-18 of pregnancy did not reduce preeclampsia incidence in the intention-to-treat paradigm. However, vitamin D levels of 30 ng/ml or higher at trial entry and in late pregnancy were associated with a lower risk of preeclampsia. Differentially expressed vitamin D-associated transcriptomes implicated the emergence of an early pregnancy, distinctive immune response in women who went on to develop preeclampsia. TRIAL REGISTRATION. ClinicalTrials.gov NCT00920621. FUNDING. Quebec Breast Cancer Foundation and Genome Canada Innovation Network. This trial was funded by the National Heart, Lung, and Blood Institute. For details see Acknowledgments., Introduction Preeclampsia is a multisystem complex disease of pregnancy with grave maternal-fetal and neonatal consequences that complicates up to 8% of pregnancies (1, 2). Low vitamin D status, among other [...]

Precision medicine has received attention both in and outside the clinic. We focus on the latter, by exploiting the relationship between individuals' social interactions and their mental health to predict one's likelihood of being depressed or anxious from rich dynamic social network data. Existing studies differ from our work in at least one aspect: they do not model social interaction data as a network; they do so but analyze static network data; they examine "correlation" between social networks and health but without making any predictions; or they study other individual traits but not mental health. In a comprehensive evaluation, we show that our predictive model that uses dynamic social network data is superior to its static network as well as non-network equivalents when run on the same data. Supplementary material for this work is available at https://nd.edu/~cone/NetHealth/PSB_SM.pdf.