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Purpose: Pelvic support osteotomy (PSO) is regarded to provide pelvic stability and improve abductor function to delay or even avoid total hip arthroplasty (THA) in young patients with high-riding hip dysplasia. However, some of these patients eventually have to undergo THA. Because of the double-angulation deformity of the femur after PSO, subsequent THA is challenging. This study aimed to analyze whether PSO surgery is suitable for high-riding hip dysplasia and summarize orthopaedic strategy during THA for patients with previous PSO. Methods: This case–control study included eight cases of THA for high-riding hip dysplasia patients with previous PSO (study group) and 24 cases of high-riding hip dysplasia patients without any hip surgical therapy (control group) by a 1:3 match (from May 2018 to January 2022). We compared demographics and joint function before and after THA between two groups and recorded all patients' preoperative imaging data, surgical procedures, postoperative imaging data, and complications. The surgical techniques for patients with previous PSO were highlighted. Results: There was no statistical difference between the two groups in demographic (p > 0.05). The study group had worse hip Harris score (HHS), range of motion (ROM), visual analogue scale (VAS), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) (p < 0.05) compared with the control group before THA. All patients had concurrent THA and osteotomy at the proximal femur, but the study group experienced longer operation time (p = 0.047) with more blood loss (p = 0.027) and higher complication rate compared with the control group (p = 0.009). At the last follow-up, the study group's HHS, ROM, VAS, and WOMAC were still worse than those in the control group. Conclusions: PSO did not improve the joint function of high-riding hip dysplasia patients but brought challenges to subsequent THA and affected the surgical outcomes. In short, we suggested that PSO is unsuitable for routine high-riding hip dysplasia patients. [ABSTRACT FROM AUTHOR]

Diagnostic tests usually need to operate at a high sensitivity or specificity level in practice. Accordingly, specificity at the controlled sensitivity, or vice versa, is a clinically sensible performance metric for evaluating continuous biomarkers. Meanwhile, the performance of a biomarker may vary across sub‐populations as defined by covariates, and covariate‐specific evaluation can be informative. In this article, we develop a novel modeling and estimation method for covariate‐specific specificity at a controlled sensitivity level. Unlike existing methods which typically adopt elaborate models of covariate effects over the entire biomarker distribution, our approach models covariate effects locally at a specific sensitivity level of interest. We also extend our proposed model to handle the whole continuum of sensitivities via dynamic regression and derive covariate‐specific ROC curves. We provide the variance estimation through bootstrapping. The asymptotic properties are established. We conduct extensive simulation studies to evaluate the performance of our proposed methods in comparison with existing methods, and further illustrate the applications in two clinical studies for aggressive prostate cancer. [ABSTRACT FROM AUTHOR]

Continuous biomarkers are common for disease screening and diagnosis. To reach a dichotomous clinical decision, a threshold would be imposed to distinguish subjects with disease from nondiseased individuals. Among various performance metrics, specificity at a controlled sensitivity level (or vice versa) is often desirable because it directly targets the clinical utility of the intended clinical test. Meanwhile, covariates, such as age, race, as well as sample collection conditions, could impact the biomarker distribution and may also confound the association between biomarker and disease status. Therefore, covariate adjustment is important in such biomarker evaluation. Most existing covariate adjustment methods do not specifically target the desired sensitivity/specificity level, but rather do so for the entire biomarker distribution. As such, they might be more prone to model misspecification. In this paper, we suggest a parsimonious quantile regression model for the diseased population, only locally at the controlled sensitivity level, and assess specificity with covariate‐specific control of the sensitivity. Variance estimates are obtained from a sample‐based approach and bootstrap. Furthermore, our proposed local model extends readily to a global one for covariate adjustment for the receiver operating characteristic (ROC) curve over the sensitivity continuum. We demonstrate computational efficiency of this proposed method and restore the inherent monotonicity in the estimated covariate‐adjusted ROC curve. The asymptotic properties of the proposed estimators are established. Simulation studies show favorable performance of the proposal. Finally, we illustrate our method in biomarker evaluation for aggressive prostate cancer. [ABSTRACT FROM AUTHOR]

We report on mode-locked thulium-doped fiber lasers with high-energy nanosecond pulses, relying on the transmission in a semiconductor saturable absorber (SESA) and a carbon nanotube (CNTs-PVA) film separately. A section of an SMF–MMF–SMF structure multimode interferometer with a transmission peak wavelength of ∼2003 nm was used as a wavelength selector to fix the laser wavelength. When the SESA acted as a saturable absorber (SA), the mode-locked fiber laser had a maximum output power of ∼461 mW with a pulse energy of ∼0.14 μJ and a pulse duration of ∼9.14 ns. In a CNT-film-based mode-locked fiber laser, stable mode-locked pulses with the maximum output power of ∼46 mW, pulse energy of ∼26.8 nJ and pulse duration of ∼9.3 ns were obtained. To the best of our knowledge, our experiments demonstrated the first 2 μm region 'real' SA-based dissipative soliton resonance with the highest mode-locked pulse energy from a 'real' SA-based all-fiberized resonator. [ABSTRACT FROM AUTHOR]

Recurrent events often arise in follow‐up studies where a subject may experience multiple occurrences of the same event. Most regression models with recurrent events tacitly assume constant effects of covariates over time, which may not be realistic in practice. To address time‐varying effects, we develop a dynamic regression model to target the mean frequency of recurrent events. We propose an estimation procedure which fully exploits observed data. Consistency and weak convergence of the proposed estimator are established. Simulation studies demonstrate that the proposed method works well, and two real data analyses are presented for illustration. [ABSTRACT FROM AUTHOR]

Background: Robot-assisted total knee arthroplasty (TKA) has been shown to facilitate high-precision bone resection, which is an important goal in TKA. The aim of this cadaveric study was to analyze the accuracy of the target angle and bone resection thickness of a recently introduced robotic TKA system. Methods: This study used 4 frozen cadaveric specimens (8 knees), 2 different implant designs, navigation, and a robotic system. The 4 surgeons who participated in this study were trained and familiar with the basic principles and operating procedures of this system. The angle of the bone cuts performed using the robotic system was compared with the target angles from the intraoperative plan. For each bone cut, the resection thickness was recorded and compared with the planned resection thickness. Results: The mean angular difference for all specimens was less than 1°, and the standard deviation was less than 2°. The mean difference between the planned and measured angles was close to 0 and not significantly different from 0 except for the difference in the frontal tibial component angle, which was 0.88°. The mean difference in the hip-knee-ankle axis angle was − 0.21°± 1.06°. The mean bone resection difference for all specimens was less than 1 mm, and the standard deviation was less than 0.5 mm. Conclusions: The results of the cadaveric experimental study showed that the new TKA system can realize highly accurate bone cuts and achieve planned angles and resection thicknesses. Despite the limitations of small sample sizes and large differences between cadaveric and clinical patients, the accuracy of cadaveric experiments provides strong support for subsequent clinical trials. [ABSTRACT FROM AUTHOR]

In order to solve the problem of path following for large ships travelling in restricted waterways, an adaptive Line-Of-Sight (LOS) guidance with the Nonlinear Terminal Sliding Mode (NTSM) algorithm is proposed in this paper. Compared to classical Sliding Mode Controller (SMC) and Adaptive Sliding Mode Controller (ASMC), the proposed system enables large ships to track the desired path much faster, meanwhile, the course stability can also be achieved with the controller saturation and environmental disturbance. To solve the problems of high nonlinearity and uncertainty of ship motion in restricted waters, the mathematical model with Extended state observer (ESO) is proposed to linearize problem. A drift angle observer is established to observe the drift angle during the vessel's navigation, and the LOS guidance law is used to achieve accurate tracking of the desired trajectory. The simulation results show that the inland ships under the control of the proposed scheme can accurately track the desired trajectory in a restricted waterway. The proposed algorithm has good robustness under the effect of environmental disturbances. • An adaptive path following control method is proposed for large inland ships travelling in restricted waterway. • The proposed system can track the desired path faster with the controller saturation and environmental disturbance. • The path following simulation tests of inland ships are carried out in a restricted waterway. • The proposed algorithm has good robustness under the effect of environmental disturbances. [ABSTRACT FROM AUTHOR]

Background: Accurate preoperative planning is crucial for successful total hip arthroplasty (THA) for developmental dysplasia of the hip (DDH). The aim of this study was to compare the accuracy of an artificial intelligence-assisted three-dimensional (3D) planning system (AIHIP) with two-dimensional templates in predicting acetabular cup size in THA for DDH. Method: This study retrospectively analyzed image data from 103 DDH patients who had THA between May 2019 and August 2023. AIHIP was used for 3D planning, and two-dimensional (2D) templates were used by two experienced surgeons. Accuracy was assessed by comparing predicted and actual cup sizes, and potential factors affecting accuracy were analyzed, including gender, side, BMI, and dysplasia classification. Results: AIHIP had higher accuracy in predicting the acetabular cup size compared to the 2D template. Within ± 0 size, AIHIP's accuracy was 84.1%, while the 2D template's was 64.0% (p < 0.05). Within ± 1 size, AIHIP's accuracy was 95.1%, while the 2D template's was 81.1% (p < 0.05). Accuracy was unaffected by gender, side, or BMI but was by DDH classification. In subgroup analysis, AIHIP's mean absolute error (0.21 ± 0.54) was significantly lower than the 2D template's (0.62 ± 0.95) for Crowe II and Crowe III (p < 0.05). Conclusion: AIHIP is superior to 2D templates in predicting the acetabular cup size accurately for THA in DDH patients. AIHIP may be especially beneficial for Crowe II and III DDH patients, as 2D templates may not accurately predict cup size in these cases. [ABSTRACT FROM AUTHOR]

Summary: Many survival studies have error‐contaminated covariates due to the lack of a gold standard of measurement. Furthermore, the error distribution can depend on the true covariates but the structure may be difficult to characterize; heteroscedasticity is a common manifestation. We suggest a novel dependent measurement error model with minimal assumptions on the dependence structure, and propose a new functional modeling method for Cox regression when an instrumental variable is available. This proposal accommodates much more general error contamination than existing approaches including nonparametric correction methods of Huang and Wang (2000, Journal of the American Statistical Association95, 1209–1219; 2006, Statistica Sinica16, 861–881). The estimated regression coefficients are consistent and asymptotically normal, and a consistent variance estimate is provided for inference. Simulations demonstrate that the procedure performs well even under substantial error contamination. Illustration with a clinical study is provided. [ABSTRACT FROM AUTHOR]

Dynamic regression models, including the quantile regression model and Aalen’s additive hazards model, are widely adopted to investigate evolving covariate effects. Yet lack of monotonicity respecting with standard estimation procedures remains an outstanding issue. Advances have recently been made, but none provides a complete resolution. In this article, we propose a novel adaptive interpolation method to restore monotonicity respecting, by successively identifying and then interpolating nearest monotonicity-respecting points of an original estimator. Under mild regularity conditions, the resulting regression coefficient estimator is shown to be asymptotically equivalent to the original. Our numerical studies have demonstrated that the proposed estimator is much more smooth and may have better finite-sample efficiency than the original as well as, when available as only in special cases, other competing monotonicity-respecting estimators. Illustration with a clinical study is provided. [ABSTRACT FROM AUTHOR]

The case-cohort design facilitates economical investigation of risk factors in a large survival study, with covariate data collected only from the cases and a simple random subset of the full cohort. Methods that accommodate the design have been developed for various semiparametric models, but most inference procedures are based on asymptotic distribution theory. Such inference can be cumbersome to derive and implement, and does not permit confidence band construction. While the bootstrap is an obvious alternative, it is unclear how to resample because of complications from the two-stage sampling design. We establish an equivalent sampling scheme, and propose a novel and versatile nonparametric bootstrap for robust inference with an appealingly simple single-stage resampling. Theoretical justification and numerical assessment are provided for a number of procedures under the proportional hazards model. [ABSTRACT FROM PUBLISHER]

ABSTRACT Weighted log-rank estimating function has become a standard estimation method for the censored linear regression model, or the accelerated failure time model. Well established statistically, the estimator defined as a consistent root has, however, rather poor computational properties because the estimating function is neither continuous nor, in general, monotone. We propose a computationally efficient estimator through an asymptotics-guided Newton algorithm, in which censored quantile regression methods are tailored to yield an initial consistent estimate and a consistent derivative estimate of the limiting estimating function. We also develop fast interval estimation with a new proposal for sandwich variance estimation. The proposed estimator is asymptotically equivalent to the consistent root estimator and barely distinguishable in samples of practical size. However, computation time is typically reduced by two to three orders of magnitude for point estimation alone. Illustrations with clinical applications are provided. [ABSTRACT FROM AUTHOR]

The accelerated failure time (AFT) model is an important regression tool to study the association between survival time and covariates. Semiparametric inference procedures have been proposed extensively in the literature. Recently, Zhou (2005a) proposed to use a model-based empirical likelihood approach to interval estimation for the AFT model. However, comparison was not made with more standard approaches based on score test and Wald-type test. In this article, we conduct extensive simulation studies to evaluate their relative performance in small samples. In addition to these methods, we also devise and consider model-free empirical likelihood approach for the comparisons. Our simulation results suggest that these empirical likelihood-based methods are not better than the standard approach based on the score test. [ABSTRACT FROM AUTHOR]

Recurrent events arise in many longitudinal medical studies where time to a terminal event or failure is the primary endpoint. With incomplete follow-up data, the analysis of recurrent events is a challenge owing to their association with the failure. One specific quantity of interest rarely addressed in the statistical literature is the recurrence frequency at the failure time: an example is hospitalization frequency, which is often used as a rough measure of lifetime medical cost. In this article we show that a marginal model (e.g., the log-linear model) of the recurrence frequency, although desirable, is typically not identifiable. For this reason, we advocate modeling the recurrent events and the failure time jointly, and propose an approach to forming semiparametric joint models from prespecified marginal ones. We suggest two conceptually simple and nested regression models aiming at the recurrence frequency as a mark of the failure and at the process of recurrent events. We formulate monotone estimating functions and propose novel interval-estimation procedures to accommodate nonsmooth estimating functions. The resulting estimators are consistent and asymptotically normal. Simulation studies and the application to an AIDS clinical trial exhibit that these proposals are easy to implement and reliable for practical use. Finally, we generalize our proposals to marked recurrent events, and also devise a global inference procedure for recurrent events of multiple types. [ABSTRACT FROM AUTHOR]

Analysis of lifetime medical cost is challenged by its irregular distribution and by incomplete follow-up, particularly the latter with limited study duration as is typical in medical research. This article proposes the so-called calibration regression to address these issues by modeling not only lifetime cost but also survival time, in a semiparametric fashion. It is postulated that both outcomes, on possibly transformed scales, linearly relate to the covariates: however, the bivariate model error distribution is completely unspecified. Whereas this conceptually simple model is a natural multivariate generalization of the accelerated failure time model, this article proposes an inference procedure by extending the weighted log-rank estimating function to the marked point process framework. The resulting estimators are consistent and asymptotically normal. In addition, robustness and efficiency issues are discussed. Furthermore, a novel sample-based variance estimation procedure is proposed for estimators based on nonsmooth estimating functions in general. Finally, simulation studies show that this proposal is reliable for practical use. An illustration with application to a lung cancer clinical trial is provided. [ABSTRACT FROM AUTHOR]

Many survival studies involve covariates that are not accurately ascertainable; CD4 lymphocyte count in HIV/AIDS research is a typical example for which the gold standard of the measurement is not available. This article proposes a consistent estimation procedure for Cox regression under the additive measurement error model. Distinct from existing methods, the proposed estimation for regression coefficients does not require any additional assumptions. We establish the normalized partial-score function as a functional of empirical processes, which facilitates the construction of an estimating function with the same limit using replicated mismeasured covariates. The resulting regression coefficient estimators are shown to be consistent and asymptotically normal; a consistent sandwich variance estimate is presented. We also suggest estimators for the baseline cumulative hazard function. Numerical studies demonstrate that the procedure performs well under practical sample sizes. Application to an AIDS clinical trial is provided. Finally, we suggest a unified approach to estimating function construction and large-sample study with partial covariate information, in light of the functional representation of the partial-score function. [ABSTRACT FROM AUTHOR]

Many medical studies involve observations of patients experiencing multiple consecutive states during their follow-up. Since treatments may have differential effects on these states, comparison is of interest with respect to individual sojourn times. In this article I generalize the univariate accelerated failure time model for multistate processes and model the treatment effects as state-specific time scale changes. I propose a two-sample inference procedure that accommodates incomplete follow-up data. This semiparametric procedure, based on estimating equations, yields a class of estimators that are consistent and asymptotically normal. Sample-based consistent variance estimates are derived. Numerical studies demonstrate that the procedure performs well for practical sample sizes. Application to a cancer clinical trial is presented for illustration. [ABSTRACT FROM AUTHOR]

Radiation enteritis remains a major challenge for radiotherapy against abdominal and pelvic malignancies. Nevertheless, there is no approved effective therapy to alleviate irradiation (IR)-induced gastrointestinal (GI) toxicity. In the current study, Cannabidiol (CBD) was found to mitigate intestinal injury by GPX4-mediated ferroptosis resistance upon IR exposure. RNA-sequencing was employed to investigate the underlying mechanism involved in the radio-protective effect of CBD, wherein runt-related transcription factor 3 (RUNX3) and its target genes were changed significantly. Further experiment showed that the transactivation of GPX4 triggered by the direct binding of RUNX3 to its promoter region, or by stimulating the transcriptional activity of NF-κB via RUNX3-mediated LILRB3 upregulation was critical for the anti-ferroptotic effect of CBD upon IR injury. Specially, CBD was demonstrated to be a molecular glue skeleton facilitating the heterodimerization of RUNX3 with its transcriptional chaperone core-biding factor β (CBFβ) thereby promoting their nuclear localization and the subsequent transactivation of GPX4 and LILRB3. In short, our study provides an alternative strategy to counteract IR-induced enteritis during the radiotherapy on abdominal/pelvic neoplasms. [Display omitted] • CBD alleviated radiation enteritis via GPX4 mediated ferroptosis. • RUNX3 directly transactivates GPX4 thereby counteracting IR-induced ferroptosis. • LILRB3 transactivated by RUNX3 triggered by CBD conferred ferroptosis resistance. • LILRB3 transactivation by RUNX3 conferred to the nuclear translocation of NF-κB. • The interaction of CBFβ with RUNX3 facilitates the ferroptosis surveillance of CBD. The heterodimerization of RUNX3 with CBFβ stimulated by CBD treatment facilitated GPX4 and LILRB3 transactivation thereby conferring ferroptosis resistance upon IR exposure. [ABSTRACT FROM AUTHOR]

In many applications, variables of interest are marks of the endpoint which are not observed when the survival time is censored. This paper focuses on nonparametric estimation of the joint distribution and summaries of survival time and mark variables. We establish a representation of the joint distribution function through the cumulative markspecilk hazard function, which is analogous to the product integral representation of univariate survival function. We identify a basic data structure common to various applications, propose nonparametric estimators and show that they maximise the likelihood. We formulate the problem in the marked point process framework and study both finite and large-sample properties of the estimators. We show that the joint distribution function estimator is nearly unbiased, uniformly strongly consistent and asymptotically normal. We also derive asymptotic variance ropose sample-based variance estimates. Numerical studies dem stimators and their variance estimates perform well for practical sample sizes. We outline an application strategy. [ABSTRACT FROM AUTHOR]

A high sensitivity fiber-optic sensor based on self-imaging effect in a hollow-core capillary waveguide (HCCW) is presented for sensing applications. The sensor is composed of a section of HCCW fusion spliced between single mode fibers (SMFs). The self-imaging effect in the HCCW is investigated with different fiber lengths and arc-fusion parameters. By infiltrating the hollow core with index matching liquids, the peak wavelength of the proposed device shifts towards longer wavelengths. The temperature and refractive index (RI) responses of the sensor are studied systematically. When temperature is increased from 25 °C to 75 °C, the temperature sensitivity of the device can be improved significantly with the infiltrated structure, and reaches −0.49 nm/°C, compared with that of the un-filled device, which is 9.8 pm/°C. For the RI response, the liquid-filled structure achieves sensitivity of 12,005 nm/RIU in the range between 1.448 and 1.450, slightly higher than the 11,920 nm/RIU achieved by the un-filled one. The proposed sensor exhibits the advantages of simple structure, high sensitivity and low cost, which may find potential applications in physical and chemical sensing. [ABSTRACT FROM AUTHOR]

Genetic improvement of livestock productivity has resulted in greater production of metabolic heat and potentially greater susceptibility to heat stress. Various studies have demonstrated that there is genetic variability for heat tolerance and genetic selection for more heat tolerant individuals is possible. The rate of genetic progress tends to be greater when genomic information is incorporated into the analyses as more accurate breeding values can be obtained for young individuals. Therefore, this study aimed (1) to evaluate the predictive ability of genomic breeding values for heat tolerance based on routinely recorded traits, and (2) to investigate the genetic background of heat tolerance based on single‐step genome‐wide association studies for economically important traits related to body composition, growth and reproduction in Large White pigs. Pedigree information was available for 265,943 animals and genotypes for 8686 animals. The studied traits included ultrasound backfat thickness (BFT), ultrasound muscle depth (MDP), piglet weaning weight (WW), off‐test weight (OTW), interval between farrowing (IBF), total number of piglets born (TNB), number of piglets born alive (NBA), number of piglets born dead (NBD), number of piglets weaned (WN) and weaning‐to‐estrus interval (IWE). The number of phenotypic records ranged from 6059 (WN) to 172,984 (TNB). Single‐step genomic reaction norm predictions were used to calculate the genomic estimated breeding values for each individual. Predictions of breeding values for the validation population individuals were compared between datasets containing phenotypic records measured in the whole range of temperatures (WR) and datasets containing only phenotypic records measured when the weather station temperature was above 10°C (10C) or 15°C (15C), to evaluate the usefulness of these datasets that may better reflect the within‐barn temperature. The use of homogeneous or heterogeneous residual variance was found to be trait‐dependent, where homogeneous variance presented the best fit for MDP, BFT, OTW, TNB, NBA, WN and IBF, while the other traits (WW and IWE) had better fit with heterogeneous variance. The average prediction accuracy, dispersion and bias values considering all traits for WR were 0.36 ± 0.05, −0.07 ± 0.13 and 0.76 ± 0.10, respectively; for 10C were 0.39 ± 0.02, −0.05 ± 0.07 and 0.81 ± 0.05, respectively; and for 15C were 0.32 ± 0.05, −0.05 ± 0.11 and 0.84 ± 0.10, respectively. Based on the studied traits, using phenotypic records collected when the outside temperature (from public weather stations) was above 10°C provided better predictions for most of the traits. Forty‐three and 62 candidate genomic regions were associated with the intercept (overall performance level) and slope term (specific biological mechanisms related to environmental sensitivity), respectively. Our results contribute to improve genomic predictions using existing datasets and better understand the genetic background of heat tolerance in pigs. Furthermore, the genomic regions and candidate genes identified will contribute to future genomic studies and breeding applications. [ABSTRACT FROM AUTHOR]

Simple Summary: We find that mutations in the TP53, FLT4, EYA1, NCOR2, CSMD3, and PCDH15 genes are associated with prostate cancer metastasis and mutations in EYA1 and CSMD3 are associated with poor outcome. Our finding that oxidative phosphorylation is associated with metastasis and identification of mutations enriched in prostate cancer metastases have implications for our understanding of the molecular events required for prostate metastasis. They also have relevance for our understanding of prostate cancer health disparities and provide a rationale for identification of compounds that target oxidative phosphorylation in metastatic prostate cancer. Prostate cancer is a highly heterogeneous disease and mortality is mainly due to metastases but the initial steps of metastasis have not been well characterized. We have performed integrative whole exome sequencing and transcriptome analysis of primary prostate tumor foci and corresponding lymph node metastases (LNM) from 43 patients enrolled in clinical trial. We present evidence that, while there are some cases of clonally independent primary tumor foci, 87% of primary tumor foci and metastases are descended from a common ancestor. We demonstrate that genes related to oxidative phosphorylation are upregulated in LNM and in African-American patients relative to White patients. We further show that mutations in TP53, FLT4, EYA1, NCOR2, CSMD3, and PCDH15 are enriched in prostate cancer metastases. These findings were validated in a meta-analysis of 3929 primary tumors and 2721 metastases and reveal a pattern of molecular alterations underlying the pathology of metastatic prostate cancer. We show that LNM contain multiple subclones that are already present in primary tumor foci. We observed enrichment of mutations in several genes including understudied genes such as EYA1, CSMD3, FLT4, NCOR2, and PCDH15 and found that mutations in EYA1 and CSMD3 are associated with a poor outcome in prostate cancer. [ABSTRACT FROM AUTHOR]

False discovery rate (FDR) control is an important tool of statistical inference in feature selection. In mass spectrometry-based metabolomics data, features can be measured at different levels of reliability and false features are often detected in untargeted metabolite profiling as chemical and/or bioinformatics noise. The traditional false discovery rate methods treat all features equally, which can cause substantial loss of statistical power to detect differentially expressed features. We propose a reliability index for mass spectrometry-based metabolomics data with repeated measurements, which is quantified using a composite measure. We then present a new method to estimate the local false discovery rate (lfdr) that incorporates feature reliability. In simulations, our proposed method achieved better balance between sensitivity and controlling false discovery, as compared to traditional lfdr estimation. We applied our method to a real metabolomics dataset and were able to detect more differentially expressed metabolites that were biologically meaningful. [ABSTRACT FROM AUTHOR]

Identifying factors associated with increased medical cost is important for many micro- and macro-institutions, including the national economy and public health, insurers and the insured. However, assembling comprehensive national databases that include both the cost and individual-level predictors can prove challenging. Alternatively, one can use data from smaller studies with the understanding that conclusions drawn from such analyses may be limited to the participant population. At the same time, smaller clinical studies have limited follow-up and lifetime medical cost may not be fully observed for all study participants. In this context, we develop new model selection methods and inference procedures for secondary analyses of clinical trial data when lifetime medical cost is subject to induced censoring. Our model selection methods extend a theory of penalized estimating function to a calibration regression estimator tailored for this data type. Next, we develop a novel inference procedure for the unpenalized regression estimator using perturbation and resampling theory. Then, we extend this resampling plan to accommodate regularized coefficient estimation of censored lifetime medical cost and develop postselection inference procedures for the final model. Our methods are motivated by data from Southwest Oncology Group Protocol 9509, a clinical trial of patients with advanced nonsmall cell lung cancer, and our models of lifetime medical cost are specific to this population. But the methods presented in this article are built on rather general techniques and could be applied to larger databases as those data become available. [ABSTRACT FROM AUTHOR]

Introduction Cross-sectional and longitudinal studies in the general population have shown that a physically active lifestyle may have anti-inflammatory properties, but evidence from studies conducted with maintenance hemodialysis (HD) patients is limited. Methods A multicenter prospective cohort of 755 HD participants aged 20-92 was evaluated in a USRDS special study 2009-2013. Kilocalories/week (kcal/week) of leisure time physical activity (LTPA) was estimated from the Minnesota Leisure Time Activity questionnaire. Predialysis serum samples were obtained concurrent with LTPA report date. Generalized estimating equations (GEE) examined association of participants' LTPA and log-normalized CRP across 24 months. Cox proportional hazards models investigated LTPA and survival over a median follow-up of 718 days. Findings Baseline median CRP concentration was lower for participants with 500+ kcal/week LTPA vs. those with <500 kcal/week LTPA (3.4 mg/L vs. 4.6 mg/L; P = 0.03). Participants who reported lower LTPA (<500 kcal/week) at both baseline and 12 months had a borderline significant increase in CRP concentration (within-group change 4.8 [1.9-10.4] to 5.8 [1.6-15.7]; P = 0.08). Lower LTPA was associated with higher log CRP over 24 months in adjusted GEE analyses (β coefficient = 0.16 [95% CI 0.02-0.31]; P = 0.03). 67/364 (18%) and 43/391 (11%) deaths occurred, respectively, among participants reporting <500 vs. 500+ kcal/week LTPA [adjusted mortality hazard ratio 1.63 (CI, 1.07, 2.47)]. Discussion The data suggest that increased estimated levels of LTPA, a physical activity/exercise opportunity widely applicable to HD patients, may be associated with lower CRP concentration as well as better survival outcome. [ABSTRACT FROM AUTHOR]

In survival analysis, quantile regression has become a useful approach to account for covariate effects on the distribution of an event time of interest. In this article, we discuss how quantile regression can be extended to model counting processes and thus lead to a broader regression framework for survival data. We specifically investigate the proposed modeling of counting processes for recurrent events data. We show that the new recurrent events model retains the desirable features of quantile regression such as easy interpretation and good model flexibility, while accommodating various observation schemes encountered in observational studies. We develop a general theoretical and inferential framework for the new counting process model, which unifies with an existing method for censored quantile regression. As another useful contribution of this work, we propose a sample-based covariance estimation procedure, which provides a useful complement to the prevailing bootstrapping approach. We demonstrate the utility of our proposals via simulation studies and an application to a dataset from the U.S. Cystic Fibrosis Foundation Patient Registry (CFFPR). Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

The microbial composition resemblance among individuals in a group can be summarized in a square covariance matrix and fitted in linear models. We investigated eight approaches to create the matrix that quantified the resemblance between animals based on the gut microbiota composition. We aimed to compare the performance of different methods in estimating trait microbiability and predicting growth and body composition traits in three pig breeds. This study included 651 purebred boars from either breed: Duroc (n = 205), Landrace (n = 226), and Large White (n = 220). Growth and body composition traits, including body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content, were measured on live animals at the market weight (156 ± 2.5 d of age). Rectal swabs were taken from each animal at 158 ± 4 d of age and subjected to 16S rRNA gene sequencing. Eight methods were used to create the microbial similarity matrices, including 4 kernel functions (Linear Kernel, LK ; Polynomial Kernel, PK ; Gaussian Kernel, GK ; Arc-cosine Kernel with one hidden layer, AK1), 2 dissimilarity methods (Bray-Curtis, BC ; Jaccard, JA), and 2 ordination methods (Metric Multidimensional Scaling, MDS ; Detrended Correspondence analysis, DCA). Based on the matrix used, microbiability estimates ranged from 0.07 to 0.21 and 0.12 to 0.53 for Duroc, 0.03 to 0.21 and 0.05 to 0.44 for Landrace, and 0.02 to 0.24 and 0.05 to 0.52 for Large White pigs averaged over traits in the model with sire, pen, and microbiome, and model with the only microbiome, respectively. The GK, JA, BC, and AK1 obtained greater microbiability estimates than the remaining methods across traits and breeds. Predictions were made within each breed group using four-fold cross-validation based on the relatedness of sires in each breed group. The prediction accuracy ranged from 0.03 to 0.18 for BW, 0.08 to 0.31 for BF, 0.21 to 0.48 for LD, and 0.04 to 0.16 for IMF when averaged across breeds. The BC, MDS, LK, and JA achieved better accuracy than other methods in most predictions. Overall, the PK and DCA exhibited the worst performance compared to other microbiability estimation and prediction methods. The current study shows how alternative approaches summarized the resemblance of gut microbiota composition among animals and contributed this information to variance component estimation and phenotypic prediction in swine. [ABSTRACT FROM AUTHOR]

We develop a simple corrected score for logistic regression with errors-in-covariates. The new method is an extension of the consistent functional methods proposed by Huang and Wang (2001) and is closely related to the corrected score method by Nakamura (1990) and Stefanski (1989). The new method requires that the measurement error distribution is known, but does not require normality. The new method yields a consistent and asymptotically normal estimator under regularity conditions. We examine the finite-sample performance of the new estimator through simulation studies. Finally, we illustrate the new method by applying it to an AIDS study. [ABSTRACT FROM PUBLISHER]

The purpose of this study was to investigate the use of feeding behavior in conjunction with gut microbiome sampled at two growth stages in predicting growth and body composition traits of finishing pigs. Six hundred and fifty-one purebred boars of three breeds: Duroc (DR), Landrace (LR), and Large White (LW), were studied. Feeding activities were recorded individually from 99 to 163 days of age. The 16S rRNA gene sequences were obtained from each pig at 123 ± 4 and 158 ± 4 days of age. When pigs reached market weight, body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content were measured on live animals. Three models including feeding behavior (Model_FB), gut microbiota (Model_M), or both (Model_FB_M) as predictors, were investigated. Prediction accuracies were evaluated through cross-validation across genetic backgrounds using the leave-one-breed-out strategy and across rearing environments using the leave-one-room-out approach. The proportions of phenotypic variance of growth and body composition traits explained by feeding behavior ranged from 0.02 to 0.30, and from 0.20 to 0.52 when using gut microbiota composition. Overall prediction accuracy (averaged over traits and time points) of phenotypes was 0.24 and 0.33 for Model_FB, 0.27 and 0.19 for Model_M, and 0.40 and 0.35 for Model_FB_M for the across-breed and across-room scenarios, respectively. This study shows how feeding behavior and gut microbiota composition provide non-redundant information in predicting growth in swine. [ABSTRACT FROM AUTHOR]

Qikou sag, located in north-center of Huanghua depression in Bohai Basin, is a Cenozoic sag with rich hydrocarbon. As a microcosm of Bohai Basin, the fault characteristics of Cenozoic structural layers in Qikou sag could indicate and record the evolution of Cenozoic stress field in Bohai Basin. Based on the latest 3-D seismic data, the study takes statistics on the fault system of Cenozoic structural layers and analyzes the fault throws of major large faults along the strikes in different periods in Qikou sag, then the fault distribution regularities and the fault direction characteristics in each structural layer are summarized. The result shows that during Cenozoic, the fault activity strength migrates from southwest to northeast and the strikes of faults changes from northwestward in Sha-3 period to nearly east-westward since Sha-1 period. [ABSTRACT FROM AUTHOR]

Background Falls among patients undergoing maintenance hemodialysis (HD) have significant consequences for quality of life and functional independence, morbidity, healthcare utilization and even mortality, but studies on the etiology of falls within large HD cohorts are limited. Methods Falls during the past 12 months were ascertained for a prevalent multi-center HD cohort (n = 762) aged 20–92 years, and associations with demographic and treatment characteristics, comorbidities, cognitive function, prescribed medications, balance tests, frailty and depressive symptoms were assessed. Results Falls were sustained by 28.4% of participants. In multivariable-adjusted analyses, participants classified as frail were over two times more likely to report falls [odds ratio (OR): 2.39, 95% confidence interval (CI): 1.22–4.71, P = 0.01], and participants with a CES-D score 18+ and/or prescribed antidepressants were over 80% more likely to be fallers (OR: 1.83, 95% CI: 1.23–2.74, P = 0.003) than were participants with a CES-D score <18 and no prescribed antidepressants. Conclusions Frailty and depressed mood, factors that are potentially modifiable, are prominently associated with falls. [ABSTRACT FROM AUTHOR]

Simple Summary: A major problem in the swine meat industry is significant weight loss due to pigs overheating. Our approach to solving this problem is breeding swine based on their ability to survive in high temperatures. We determined in this study that genetically there is a difference between the most successful animal in hot conditions and the most successful animal in cooler conditions. This can be applied to selection of breeding animals for swine companies. Additionally, we found a few genes that may be related to this heat resistance, but more studies need to be completed to validate this finding. The purpose of this study was to perform a genome-wide association study to determine the genomic regions associated with heat stress tolerance in swine. Phenotypic information on carcass weight was available for 227,043 individuals from commercial farms in North Carolina and Missouri, U.S. Individuals were from a commercial cross of a Duroc sire and a dam resulting from a Landrace and Large White cross. Genotypic information was available for 8232 animals with 33,581 SNPs. The pedigree file contained a total of 553,448 animals. A threshold of 78 on the Temperature Humidity Index (THI) was used to signify heat stress. A two-trait analysis was used with the phenotypes heat stress (Trait One) and non-heat stress (Trait Two). Variance components were calculated via AIREML and breeding values were calculated using single step GBLUP (ssGBLUP). The heritability for Traits One and Two were calculated at 0.25 and 0.20, respectively, and the genetic correlation was calculated as 0.63. Validation was calculated for 163 genotyped sires with progeny in the last generation. The benchmark was the GEBV with complete data, and the accuracy was determined as the correlation between the GEBV of the reduced and complete data for the validation sires. Weighted ssGBLUP did not increase the accuracies. Both methods showed a maximum accuracy of 0.32 for Trait One and 0.54 for Trait Two. Manhattan Plots for Trait One, Trait Two, and the difference between the two were created from the results of the two-trait analysis. Windows explaining more than 0.8% of the genetic variance were isolated. Chromosomes 1 and 14 showed peaks in the difference between the two traits. The genetic correlation suggests a different mechanism for Hot Carcass Weight under heat stress. The GWAS results show that both traits are highly polygenic, with only a few genomic regions explaining more than 1% of variance. [ABSTRACT FROM AUTHOR]

The article investigates how end-stage renal disease sufferers in the U.S. can be made more aware of the promising potential that peritoneal dialysis (PD) therapy holds for them, despite the low rates of preference that it holds compared to hemodialysis (HD). It offers general information about the study, the research method and study population that was selected, and the statistical method that was employed to analyze the results. Results showed that PD was often not selected by ESRD patients, and that trend should be reversed in PD's favor.

We estimated the survival and hospitalization among frequent hemodialysis users in comparison to those patients undergoing thrice-weekly conventional hemodialysis. All patients had similar characteristics and medical histories. In this cohort study of frequent hemodialysis users and propensity score–matched controls, the collaborating clinicians identified 94 patients who used nocturnal hermodialysis (NHD) and 43 patients who used short-duration daily hemodialysis (SDHD) for a minimum of 60 days. Ten propensity score–matched control patients for each NHD and SDHD patient were identified from the United States Renal Data System database. Primary outcomes were risk for all-cause mortality and risk for the composite outcome of mortality or major morbid event (acute myocardial infarction or stroke) estimated using Cox proportional hazards models. Risks for all-cause, cardiovascular-related, infection-related, and vascular access–related hospital admissions were also studied. Nocturnal hemodialysis was associated with significant reductions in mortality risk and risk for mortality or major morbid event when compared to conventional hemodialysis. There was a reduced but non-significant risk of death for patients using SDHD compared to controls. All-cause and specific hospitalizations did not differ significantly between NHD and SDHD patients and their matched control cohorts. Our study suggests that NHD may improve patient survival. [ABSTRACT FROM AUTHOR]

Background: The interplay between the gut microbiota and feeding behavior has consequences for host metabolism and health. The present study aimed to explore gut microbiota overall influence on feeding behavior traits and to identify specific microbes associated with the traits in three commercial swine breeds at three growth stages. Feeding behavior measures were obtained from 651 pigs of three breeds (Duroc, Landrace, and Large White) from an average 73 to 163 days of age. Seven feeding behavior traits covered the information of feed intake, feeder occupation time, feeding rate, and the number of visits to the feeder. Rectal swabs were collected from each pig at 73 ± 3, 123 ± 4, and 158 ± 4 days of age. DNA was extracted and subjected to 16 S rRNA gene sequencing. Results: Differences in feeding behavior traits among breeds during each period were found. The proportion of phenotypic variances of feeding behavior explained by the gut microbial composition was small to moderate (ranged from 0.09 to 0.31). A total of 21, 10, and 35 amplicon sequence variants were found to be significantly (q-value < 0.05) associated with feeding behavior traits for Duroc, Landrace, and Large White across the three sampling time points. The identified amplicon sequence variants were annotated to five phyla, with Firmicutes being the most abundant. Those amplicon sequence variants were assigned to 28 genera, mainly including Christensenellaceae_R-7_group, Ruminococcaceae_UCG-004, Dorea, Ruminococcaceae_UCG-014, and Marvinbryantia. Conclusions: This study demonstrated the importance of the gut microbial composition in interacting with the host feeding behavior and identified multiple archaea and bacteria associated with feeding behavior measures in pigs from either Duroc, Landrace, or Large White breeds at three growth stages. Our study provides insight into the interaction between gut microbiota and feeding behavior and highlights the genetic background and age effects in swine microbial studies. [ABSTRACT FROM AUTHOR]

Measuring a biomarker in pooled samples from multiple cases or controls can lead to cost-effective estimation of a covariate-adjusted odds ratio, particularly for expensive assays. But pooled measurements may be affected by assay-related measurement error (ME) and/or pooling-related processing error (PE), which can induce bias if ignored. Building on recently developed methods for a normal biomarker subject to additive errors, we present two related estimators for a right-skewed biomarker subject to multiplicative errors: one based on logistic regression and the other based on a Gamma discriminant function model. Applied to a reproductive health dataset with a right-skewed cytokine measured in pools of size 1 and 2, both methods suggest no association with spontaneous abortion. The fitted models indicate little ME but fairly severe PE, the latter of which is much too large to ignore. Simulations mimicking these data with a non-unity odds ratio confirm validity of the estimators and illustrate how PE can detract from pooling-related gains in statistical efficiency. These methods address a key issue associated with the homogeneous pools study design and should facilitate valid odds ratio estimation at a lower cost in a wide range of scenarios. [ABSTRACT FROM AUTHOR]

This study aimed to investigate interpopulation variation due to sex, breed and age, and the intrapopulation variation in the form of genetic variance for recombination in swine. Genome‐wide recombination rate and recombination occurrences (RO) were traits studied in Landrace (LR) and Large White (LW) male and female populations. Differences were found for sex, breed, sex‐breed interaction, and age effects for genome‐wide recombination rate and RO at one or more chromosomes. Dams were found to have a higher genome‐wide recombination rate and RO at all chromosomes than sires. LW animals had higher genome‐wide recombination rate and RO at seven chromosomes but lower at two chromosomes than LR individuals. The sex‐breed interaction effect did not show any pattern not already observable by sex. Recombination increased with increasing parity in females, while in males no effect of age was observed. We estimated heritabilities and repeatabilities for both investigated traits and obtained the genetic correlation between male and female genome‐wide recombination rate within each of the two breeds studied. Estimates of heritability and repeatability were low (h2 = 0.01–0.26; r = 0.18–0.42) for both traits in all populations. Genetic correlations were high and positive, with estimates of 0.98 and 0.94 for the LR and LW breeds, respectively. We performed a GWAS for genome‐wide recombination rate independently in the four sex/breed populations. The results of the GWAS were inconsistent across the four populations with different significant genomic regions identified. The results of this study provide evidence of variability for recombination in purebred swine populations. [ABSTRACT FROM AUTHOR]

The goals of the study were to estimate the heritability and repeatability of boar sperm quality traits during the summer season, estimate phenotypic and genetic correlations between sperm quality traits, and assess the effect of these traits on total number born (TNB). Semen samples were collected and individually processed from Duroc boars (n = 363) from May through October 2017. Single sire litter information was available for 121 of the boars. Heritability, repeatability, genetic correlations, and phenotypic correlations were estimated for and among the following traits: total percentage of motile cells (MOT), total percentage of progressively motile cells (PROG), percentage of cells with a distal droplet (DIST), percentage of cells with a proximal droplet (PROX), percentage of cells with a bent tail (BENT), percentage of cells with a distal midpiece reflex (DMR), mean sperm head elongation (ELON), and total sperm cells per ejaculate (TOTSP). Heritability estimates ranged from 0.08 to 0.24 and repeatability estimates ranged from 0.21 to 0.62. The phenotypic and genetic correlations between sperm motility traits and morphological defects indicated a negative relationship. Sperm morphological defects had positive phenotypic and genetic correlations with each other. Total sperm per ejaculate showed positive genetic correlations with the motility traits. Positive phenotypic and genetic correlations existed between ELON and the motility traits. Total sperm motility (P < 0.001) positively affected TNB while DIST (P < 0.001), PROX (P < 0.001), BENT (P = 0.05) and DMR (P = 0.002) negatively affected TNB. Results indicate genetic selection could enhance sperm quality during the summer season in boars. [ABSTRACT FROM AUTHOR]

Objective. To investigate the effect of FTZ on high-glucose-induced oxidative stress and underlying mechanisms. Methods. We used a β cell dysfunction and diabetes model that was induced in rats fed a high-fat high-sugar diet (HFHSD) for 6 weeks and injected once with 35 mg/kg streptozocin (STZ). Then, 3 and 6 g/kg of FTZ were administered by gavage for 8 weeks. In addition, an ex vivo model of oxidative stress was induced by stimulating INS-1 cells with 25 mmol/L glucose for 48 h. Result. The levels of fasting blood glucose (FBG) in diabetic model rats were obviously higher than those in the normal group; furthermore with reduced levels of β cells, catalase (CAT), superoxide dismutase (SOD), and Bcl-2 increased lipid peroxide malondialdehyde (MDA) and caspase-3 in the pancreatic tissue of the diabetic model rats. Afterward, the cells were incubated with FTZ-containing serum and edaravone. The 25 mmol/L glucose-induced SOD reduction increased MDA and intracellular ROS. The protein expression level of Mn-SOD and CAT in the model group decreased significantly compared with that in the control group. Conclusion. FTZ treatment significantly improved the alteration in the level of SOD, CAT, Bcl-2, caspase-3, and MDA coupled with β cell dysfunction in diabetic rats. Oxidative stress in INS-1 cells was closely associated with a higher rate of apoptosis, increased production of ROS and MDA, enhanced Bax expression, and caspase-3, -9 activities and markedly decreased protein expression of Mn-SOD and CAT. FTZ-containing serum incubation notably reversed the high-glucose-evoked increase in cell apoptosis, production of ROS and MDA, and Bax protein levels. Furthermore, FTZ stimulation upregulated the expression levels of several genes, including Mn-SOD, CAT, and Bcl-2/Bcl-xl. In addition, FTZ decreased the intracellular activity of caspase-3, -9 in INS-1 cells. FTZ protected β-cells from oxidative stress induced by high glucose in vivo and in vitro. The beneficial effect of FTZ was closely associated with a decrease in the activity of caspase-3, -9 and intracellular production of ROS, MDA, and Bax coupled with an increase in the expression of Mn-SOD, CAT, and Bcl-2/Bcl-xl. [ABSTRACT FROM AUTHOR]

The Bohai Bay and Hehuai (southern North China) rift basins in the eastern part of the North China Craton are south-north-adjacent. They have shown synchronous evolutionary processes, and possess generally identical superficial and shallow structural characteristics as well as similar basin areas. However, there is a large difference in the richness of oil resources between the two basins. The Bohai Bay Basin has extremely abundant oil reserves, while commercial oil reserves have not been found in the Hehuai Basin. The deep tectonic structures, magmatic activities, and modern and paleogeothermal fields of the two basins are significantly different. Compared with the Hehuai Basin, the Bohai Bay Basin has a thinner crust and more complex structure with multiple low-velocity layers. It is also characterized by intense magmatic activity, high modern and paleogeothermal fields, frequent seismic activity, and active deep interactions, small effective elastic thickness of the isotropic lithosphere, and shorter balanced transformation wavelength of the lithosphere with a high likelihood of local compensation. The Hehuai Basin has a simple deep structure and homogeneous crustal composition, with a high likelihood of regional compensation. The characteristics of the deep structures mentioned above are generally similar to those of the southern part of the stable Ordos Basin, except for the smaller crust thickness. This indicates the presence of differences in Mesozoic destruction between the southern and northern zones in the eastern part of the North China Craton. The northern zone was subjected to significant destruction, while the southern zone was subjected to modifications, primarily in the form of local changes in the structures and/ or properties of the crust or lithospheric mantle, with the overall structure and stability of the craton kept intact. The formation of high-quality source rock is primarily influenced by the abnormal flourishment of organisms in water bodies during the syndepositional period, and is also strongly associated with the high geothermal setting of basins and nutrients from hydrothermal solutions and volcanoes. In other words, it is mainly controlled by deep processes and deep-major fault activity. The differences in the deep structures and modern and paleo geothermal fields of the two basins correspond to the difference in richness of oil resources, suggesting that there is an important internal or causal relationship between the two aspects. This viewpoint coincides with the conditions and environments required for the development of high-quality source rock in hydrocarbon-rich basins (sags) in China and other countries, and is evidenced by the modern lake basin of the East African Rift. A new hydrocarbon generation model is proposed in this work: petroleum is a comprehensive product of the integration of bioenergy, thermal energy, and other related energies (such as chemical and kinetic energy) and their interactions; the degree of richness of petroleum is generally controlled by the regional tectonic structure, thermal environment, and deep processes; nonmarine basins or depressions with abundant resources are closely related to active deep processes, intense exchange of material between the deep and shallow layers, participation of external hydrocarbons, and energy integration and conversion. [ABSTRACT FROM AUTHOR]

In a multivariable logistic regression setting where measuring a continuous exposure requires an expensive assay, a design in which the biomarker is measured in pooled samples from multiple subjects can be very cost effective. A logistic regression model for poolwise data is available, but validity requires that the assay yields the precise mean exposure for members of each pool. To account for errors, we assume the assay returns the true mean exposure plus a measurement error (ME) and/or a processing error (PE). We pursue likelihood-based inference for a binary health-related outcome modeled by logistic regression coupled with a normal linear model relating individual-level exposure to covariates and assuming that the ME and PE components are independent and normally distributed regardless of pool size. We compare this approach with a discriminant function-based alternative, and we demonstrate the potential value of incorporating replicates into the study design. Applied to a reproductive health dataset with pools of size 2 along with individual samples and replicates, the model fit with both ME and PE had a lower AIC than a model accounting for ME only. Relative to ignoring errors, this model suggested a somewhat higher (though still nonsignificant) adjusted log-odds ratio associating the cytokine MCP-1 with risk of spontaneous abortion. Simulations modeled after these data confirm validity of the methods, demonstrate how ME and particularly PE can reduce the efficiency advantage of a pooling design, and highlight the value of replicates in improving stability when both errors are present. [ABSTRACT FROM AUTHOR]

Background: Severe secondary hyperparathyroidism, which is associated with life-threatening complications, can develop in dialysis-dependent end-stage renal disease patients. The aim of this study was to compare short- and long-term mortality in dialysis patients who underwent near-total parathyroidectomy (NTPTX) and matched nonoperated controls. Study Design: We identified 150 dialysis patients who underwent NTPTX (1993−2009) at our institution and compared them with 1,044 nonoperated control patients identified in the US Renal Data System registry, matched for age, sex, race, diabetes as cause of kidney failure, years on dialysis, and dialysis modality. Survival outcomes were estimated using multivariable Cox proportional hazards models with stratification on the matching sets, adjusted for cardiovascular comorbidities, smoking, inability to ambulate/transfer, and payor status. Results: During a follow-up of a mean of 3.6 years (range 0.1 month to 16.4 years), NTPTX patients had a significant reduction in the long-term risk of all-cause death (hazard ratio = 0.68; 95% CI, 0.52−0.89; p = 0.006) compared with controls. Thirty-day mortality rates for NTPTX patients and controls were 246 vs 105 per 1,000 person-years (p = 0.21). In adjusted analyses, NTPTX patients had a 37% reduced risk of all-cause death and a 33% reduced risk of cardiovascular death compared with controls. A durable reduction in mean parathyroid hormone was observed after NTPTX; from 1,776 ± 1,416.6 pg/mL to 301 ± 285.7 pg/mL (p < 0.0001). Conclusions: In our center, NTPTX in dialysis patients was associated with a significant reduction in long-term risk of death compared with matched control patients, without a significantly increased short-term risk. [Copyright &y& Elsevier]

Renshen Shouwu extract (RSSW) is a patented Traditional Chinese Medicine included in Chinese Pharmacopoeia for neurasthenia, forgetfulness, insomnia, inappetence and excessive fatigue. Our previous study had demonstrated the neuroprotective effect of RSSW against ischemic stroke in rats with middle cerebral artery occlusion (MCAO). However, its underlying mechanism remains unknown. In this study, we investigated the neurogenesis and angiogenesis effects of RSSW in ischemic stroke rats, and further revealed its underlying mechanism focused on TLR4/NF-κB/NLRP3 signaling pathway. Firstly, active compounds of RSSW were determined by High Performance Liquid Chromatography (HPLC). Secondly, Middle cerebral artery occlusion (MCAO) was performed to induce ischemic stroke in rats and 2, 3, 5-Triphenyltetrazolium chloride (TTC) staining was employed to evaluate whether MCAO surgery was successfully established. Neurological deficit evaluation was conducted according to the Zea Longa' method. Then, we explored the neurogenesis and angiogenesis effects after oral administration of RSSW (50 mg/kg, 100 mg/kg) in MCAO-induced rats by Immunofluorescence Staining. Moreover, the proteins involved in TLR4/NF-κB/NLRP3 signaling pathway (TLR4, p–NF–κB p65, NF-κB p65, NLRP3, pro-IL-1β, IL-1β, pro-Caspase-1, Caspase-1) were determined by western blotting. It was observed that RSSW treatment significantly increased the number of newborn neurons and brain microvessel density (MVD) after ischemic stroke. What's more, RSSW treatment significantly downregulated TLR4, p–NF–κB p65/p65, NLRP3, pro-IL-1β, IL-1β, pro-Caspase-1, Caspase-1 proteins involved in TLR4/NF-κB/NLRP3 signaling pathway. RSSW enhances neurogenesis and angiogenesis via inhibition of TLR4/NF-κB/NLRP3 inflammatory signaling pathway following ischemic stroke in rats. Hence, RSSW may be a promising Chinese Medicine for the treatment of ischemic stroke. Image 1 [ABSTRACT FROM AUTHOR]

A highly sensitive torsion sensor can be constructed by combining a twisted photonic crystal fiber with a liquid-filled waveguide in its air-hole cladding. The torsion sensitivity of this type of sensor is determined directly by the phase-matching conditions between the fiber core mode and the liquid waveguide mode, which can be improved by tuning the helicity (denoted by the initial twist rate, α0) of the twisted photonic crystal fiber. The enhancement mechanism of α0 on the sensitivity of the proposed torsion sensor is investigated theoretically, followed by experimental verifications, and a torsion sensitivity as high as 446 nm∙mm∙rad−1 can be obtained by tailoring these parameters. Experimental results show that the torsion sensitivity increases with α0 decreasing from 3.142 to 3.925 rad/mm, which are in consistence with that of the numerical predictions. The demonstrated torsion sensor is expected to contribute to the development of highly sensitive torsion-related photonic crystal fiber devices. [ABSTRACT FROM AUTHOR]

Genomic selection increases accuracy and decreases generation interval, speeding up genetic changes in the populations. However, intensive changes caused by selection can reduce the genetic variation and can strengthen undesirable genetic correlations. The purpose of this study was to investigate changes in genetic parameters for fitness traits related with prolificacy (FT1) and litter survival (FT2 and FT3), and for growth (GT1 and GT2) traits in pigs over time. The data set contained 21,269 (FT1), 23,246 (FT2), 23,246 (FT3), 150,492 (GT1), and 150,493 (GT2) phenotypic records obtained from 2009 to 2018. The pedigree file included 369,776 animals born between 2001 and 2018, of which 39,103 were genotyped. Genetic parameters were estimated with bivariate models (FT1-GT1, FT1-GT2, FT2-GT1, FT2-GT2, FT3-GT1, and FT3-GT2) using 3-yr sliding subsets. With a Bayesian implementation using the GIBBS3F90 program computations were performed as genomic analysis (GEN) or pedigree-based analysis (PED), that is, with or without genotypes, respectively. For GEN (PED), the changes in heritability from the first to the last year interval, that is, from 2009–2011 to 2015–2018 were 8.6 to 5.6 (7.9 to 8.8) for FT1, 7.8 to 7.2 (7.7 to 10.8) for FT2, 11.4 to 7.6 (10.1 to 7.5) for FT3, 35.1 to 16.5 (32.5 to 23.7) for GT1, and 35.9 to 16.5 (32.6 to 24.1) for GT2. Differences were also observed for genetic correlations as they changed from −0.31 to −0.58 (−0.28 to −0.73) for FT1-GT1, −0.32 to −0.50 (−0.29 to −0.74) for FT1-GT2, −0.27 to −0.45 (−0.30 to −0.65) for FT2-GT1, −0.28 to −0.45 (−0.32 to −0.66) for FT2-GT2, 0.14 to 0.17 (0.11 to 0.04) for FT3-GT1, and 0.14 to 0.18 (0.11 to 0.05) for FT3-GT2. Strong selection in pigs reduced heritabilities and emphasized the antagonistic genetic relationships between fitness and growth traits. With genotypes considered, heritability estimates were smaller and genetic correlations were greater than estimates with only pedigree and phenotypes. When selection is based on genomic information, genetic parameters estimated without this information can be biased because preselection is not accounted for by the model. [ABSTRACT FROM AUTHOR]

Ginkgo biloba leaves and Panax ginseng are Chinese medicine commonly used in combination for cerebral disease. To investigate the effect of standard extract of Ginkgo biloba leaves (EGb) on facilitating brain uptake of ginsenoside and its underlying mechanisms. The increasing uptake of ginsenosides in the brain of rats by EGb were detected by LC–MS/MS analysis. Evans blue and FITC-dextran leakage were determined to evaluate blood-brain barrier (BBB) permeability in vivo. Transendothelial electrical resistance (TEER) and Na–F penetration rate were measured with a co-culture of the human cerebral microvascular endothelial cell line (hCMEC/D3) and human normal glial cell line (HEB) in vitro BBB model. WB were used to analyzed the expression of BBB tight junctions (TJs) related protein (ZO-1, Occludin, Claudin-3, p-ERM, and p-MLC), ultrastructure of TJs was determined by transmission electron microscope. LC–MS/MS analysis demonstrated that EGb could improve brain uptake of ginsenoside Rg1, Re, Rd and Rb1. In vivo study showed that, BBB permeability was significantly increased after EGb administration, evidenced by the markedly increased penetration of FITC-dextran and Evans Blue into the mice brain parenchyma. In the in vitro BBB model, reduced TEER and increased Na–F penetration rate was observed in EGb group, which was associated with alteration of TJs ultrastructure. Furthermore, the expression of p-ERM and p-MLC in hCMEC/D3 as well as mice brain microvessels were significantly upregulated, but no significant change on the expression of TJs proteins (ZO-1, Occludin and Claudin-3). Moreover, the effect of EGb on in vitro BBB permeability and ERM, MLC phosphorylation was counteracted by DPCPX, an A1 adenosine receptor (A1R) antagonist. EGb might induce ERM/MLC phosphorylation and increase the cell-cell junction gaps to cause a reversible increase of the BBB permeability via A1R signaling pathway. Our results may contribute to better use of EGb in the treatment of brain diseases. Image 1 [ABSTRACT FROM AUTHOR]

Background: In this study we integrated the CNV (copy number variation) and WssGWAS (weighted single-step approach for genome-wide association) analyses to increase the knowledge about number of piglets born alive, an economically important reproductive trait with significant impact on production efficiency of pigs. Results: A total of 3892 samples were genotyped with the Porcine SNP80 BeadChip. After quality control, a total of 57,962 high-quality SNPs from 3520 Duroc pigs were retained. The PennCNV algorithm identified 46,118 CNVs, which were aggregated by overlapping in 425 CNV regions (CNVRs) ranging from 2.5 Kb to 9718.4 Kb and covering 197 Mb (~ 7.01%) of the pig autosomal genome. The WssGWAS identified 16 genomic regions explaining more than 1% of the additive genetic variance for number of piglets born alive. The overlap between CNVR and WssGWAS analyses identified common regions on SSC2 (4.2–5.2 Mb), SSC3 (3.9–4.9 Mb), SSC12 (56.6–57.6 Mb), and SSC17 (17.3–18.3 Mb). Those regions are known for harboring important causative variants for pig reproductive traits based on their crucial functions in fertilization, development of gametes and embryos. Functional analysis by the Panther software identified 13 gene ontology biological processes significantly represented in this study such as reproduction, developmental process, cellular component organization or biogenesis, and immune system process, which plays relevant roles in swine reproductive traits. Conclusion: Our research helps to improve the understanding of the genetic architecture of number of piglets born alive, given that the combination of GWAS and CNV analyses allows for a more efficient identification of the genomic regions and biological processes associated with this trait in Duroc pigs. Pig breeding programs could potentially benefit from a more accurate discovery of important genomic regions. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer (PCa) is the most common cancer affecting men in the United States. The initial treatment and subsequent monitoring of PCa patients places a large burden on U.S. health care systems. The objectives of this study were to estimate the total and disease-related per-patient lifetime costs using a phase-based model of cancer care for PCa patients enrolled in Medicare.Methods: A model was developed to estimate life-time costs for patients diagnosed with PCa. Patients ≥ 65 years old and diagnosed with PCa between calendar years 1991-2002 were selected from the SEER database. Using SEER, we estimated survival times for PCa patients from diagnosis until death. The period of time patients contributed to treatment phases was determined using an algorithm designed to model the natural history of PCa. Costs were obtained from the US SEER-Medicare database and estimated during specific phases of care. Cost estimates were then combined with survival data to yield total and PCa-related life-time costs.Results: Overall, the model estimated life-time costs of $110,520 (95% CI 110,324-110,739) per patient. PCa-related costs made up approximately 31% of total costs ($34,432).Conclusions: Prostate cancer places a significant burden on U.S. health-care systems with average life-time PCa-related costs in excess of $30,000. [ABSTRACT FROM AUTHOR]