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Chemokine receptors, a subfamily of G-protein-coupled receptors (GPCRs), are responsible for cell migration during physiological processes as well as in diseases like inflammation and cancers. Here, we present a protocol for solubilizing, purifying, and reconstituting complexes of chemokine receptors with their ligands in nanodiscs, soluble lipid bilayers that mimic the native environment of membrane receptors. The protocol yields chemokine receptor complexes with sufficient purity and yield for structural and biophysical studies and should be applicable to other GPCRs.

Alpha‐fetoprotein (AFP) is a structural serum glycoprotein that plays vital roles in reproduction and mammalian development. Analysis of serum prolactin (PRL) is considered one of the useful methods for diagnosing pregnancy in Asian elephants. However, the expression profiles of AFP in pregnant and nonpregnant Asian elephants remain unclear, nor is the relationship with PRL. In this study, serum seven gonadal hormones and AFP in three pregnant and seven nonpregnant Asian elephants were analysed by via radioimmunoassay (RIA) and enzyme‐linked immunosorbent (ELISA) assay. We found that the mean (±SD) concentration of prolactin (PRL) in pregnant (136.782 ± 30.987 ng/mL) elephants was significantly higher than that in nonpregnant elephants (52.803 ± 21.070 ng/mL; p ≤ 0.0005). The mean (±SD) concentration of AFP in pregnant elephants (11.598 ± 0.824 ng/mL) was significantly higher than that in nonpregnant elephants (7.200 ± 2.283 ng/mL; p ≤ 0.05). Furthermore, the AFP concentration was positively correlated with the PRL concentration in the 10 Asian elephants studied. In conclusion, our findings suggest that serum AFP concentration is a potential biomarker of pregnancy outcomes in Asian elephants. [ABSTRACT FROM AUTHOR]

We introduce a novel semiparametric efficient estimation procedure for high‐dimensional partial linear regression models to overcome the challenge of efficiency loss of the traditional least‐squares based estimation procedure under unknown error distributions, while enjoying several appealing theoretical properties. The new estimation procedure provides a sparse estimator for the parametric component and achieves the semiparametric efficiency as the oracle maximum likelihood estimator as if the error distribution was known. By employing the penalized estimation and the semiparametric efficiency theory for ultra‐high‐dimensional partial linear model, the procedure enjoys the oracle variable selection property and offers efficiency gain for non‐Gaussian random errors, while maintaining the same efficiency as the least squares‐based estimator for Gaussian random errors. Extensive simulation studies and an empirical application are conducted to demonstrate the effectiveness of the proposed procedure. [ABSTRACT FROM AUTHOR]

Due to the limitations of planar mode wharfs, this paper proposes a new type of wharf—the three-dimensional cantilevered wharf. The proposed wharf is defined as an improvement of the traditional wharf, extending the traditional wharf upward and cantilevering out to the sea. The three-dimensional cantilevered wharf is a composite truss structure that meets structural and functional requirements. The composite truss structure is formed by connecting the beams of frame structures as a whole. The material consumption of the three-dimensional cantilevered wharf is decreased by controlling stresses and optimizing components. A finite element model of the proposed wharf, with a length of 200 m, width of 105 m, and cantilever length of 80 m, was established to analyze the basic mechanical performance. In this paper, the force distribution of the pile foundation, the vertical force transfer efficiency of web members, the structural stiffness, and the natural frequencies of the proposed wharf are analyzed. Tests regarding the stress and stiffness of different plane cantilever trusses are carried out, and finite element analysis is used for comparison. The test results show that the high-rise cantilever truss has a great in-plane stiffness and a reasonable component stress distribution. Additionally, the three-dimensional cantilevered wharf significantly improves the utilization efficiency of the wharf. [ABSTRACT FROM AUTHOR]

Functional linear models are important tools for studying the relationship between functional response and covariates. However, if subjects come from an inhomogeneous population that demonstrates different linear relationship between the response and covariates among different subpopulations/clusters, a single functional linear model is no longer adequate for the data. A new class of mixtures of functional linear models for the analysis of heterogeneous functional data is introduced. Identifiability is established for the proposed class of mixture models under mild conditions. The proposed estimation procedures combine the ideas of local kernel regression, functional principal component analysis and EM algorithm. A generalized likelihood ratio test based on a conditional bootstrap is given as to whether the regression coefficient functions are constant. A Monte Carlo simulation study is conducted to examine the finite sample performance of the new methodology. Finally, the analysis of CO2-GDP data reveals the dynamic patterns of relationship between CO2 and GDP among different countries.

When the functional data are not homogeneous, e.g., there exist multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this paper, we propose a new estimation procedure for the Mixture of Gaussian Processes, to incorporate both functional and inhomogeneous properties of the data. Our method can be viewed as a natural extension of high-dimensional normal mixtures. However, the key difference is that smoothed structures are imposed for both the mean and covariance functions. The model is shown to be identifiable, and can be estimated efficiently by a combination of the ideas from EM algorithm, kernel regression, and functional principal component analysis. Our methodology is empirically justified by Monte Carlo simulations and illustrated by an analysis of a supermarket dataset.

Two new marine nematode species belonging to the genus Halichoanolaimus from the intertidal zone of the Yellow Sea are described. Halichoanolaimus sinensis sp. nov. is characterized by amphideal fovea with 2.5–3.0 turns, 20–27% of corresponding body diameters; spicules curved, middle portion broad, tapering distally, 1.4–1.5 cloacal body diameters long; gubernaculum slender consisting of two detached lateral pieces tapering distally; 10–13 papilliform precloacal supplements in two groups, the posterior three supplements smaller and closer to each other, the remaining supplements larger and widely spaced; tail conico-cylindrical with a half cylindrical portion. The second new species, Halichoanolaimus zhangi sp. nov. is distinct by having lateral differentiation present except in anterior half of pharynx which has even punctations, amphideal fovea with 3.0–3.3 turns, spicules curved, gradually narrowing from proximal to distal end with pointed tip, 7 papilliform precloacal supplements, gradually increasing the spacing distance forward, two rows of subventral conical setae situated at the precloacal region, tail elongated, filiform. An updated key to 30 valid species of Halichoanolaimus is provided. [ABSTRACT FROM AUTHOR]

Megaproject Social Responsibility (MSR) is widely acknowledged as contributing to project performance. However, the effect of Megaproject Social Responsibility Behavior (MSRB) implemented by organizations participating in construction on project performance remains a subject of considerable debate, and the intrinsic mechanism of MSRB's effect on the performance of megaprojects has not been elucidated. Therefore, this study employs resource-based theory to investigate the mechanism underlying MSRB's effect on project performance, taking into account both internal and external social capital as well as resource integration capacity as pivotal influences. Drawing on sample data from 206 experienced project managers across the various parties involved, this study develops a Bayesian network model to elucidate the MSRB effect mechanism. Through inference and sensitivity analysis, this study discovers variations in the enhancement effects across the four dimensions of MSRB on project performance. Notably, a combination strategy yields superior enhancement effects. Furthermore, when project performance is suboptimal, resource integration capacity emerges as a significant mediator between MSRB and project performance. Conversely, at high levels of project performance, MSRB directly contributes to enhancing project outcomes. The findings of this study offer valuable insights for the governance of MSR and the enhancement of project performance in megaprojects. [ABSTRACT FROM AUTHOR]