Previous observational studies have suggested an association between natural hair color and the risk of endometriosis; however, the causal relationship remains unclear. Here, we conducted a two-sample Mendelian randomization (MR) study to evaluate the potential causal link between natural hair color and endometriosis using 428 single nucleotide polymorphisms (SNPs) as genetic instruments derived from a genomewide meta-analysis comprising over 4511 cases and 227,260 controls of European ancestry. Our findings indicate that dark brown hair is associated with a decreased risk of developing endometriosis (dark brown IVW OR: 0.844, 95% CI [0.725, 0.984], p < .05). Conversely, dark hair color and lighter hair colors (red, blonde, and light brown) did not demonstrate a significant association with endometriosis risk (dark IVW OR: 0.568, 95% CI [0.280, 1.15], p = .117; red IVW OR: 1.058, 95% CI [0.719, 1.558], p = .77; blonde IVW OR: 1.158, 95% CI [0.886, 1.514], p = .28; light brown IVW OR: 1.306, 95% CI [0.978, 1.743], p = .07). These results provide compelling MR evidence supporting a causal association between natural hair color and endometriosis risk. Our findings underscore the need for larger scale studies and randomized controlled trials to delineate the biological mechanisms driving the association between hair color and endometriosis.

Let $\overline {M}(a,c,n)$ denote the number of overpartitions of n with first residual crank congruent to a modulo c with $c\geq 3$ being odd and $0\leq a<c$. The central objective of this paper is twofold: firstly, to establish an asymptotic formula for the crank of overpartitions; and secondly, to establish several inequalities concerning $\overline {M}(a,c,n)$ that encompasses crank differences, positivity, and strict log-subadditivity.

Diagnostic classification models (DCMs) have seen wide applications in educational and psychological measurement, especially in formative assessment. DCMs in the presence of testlets have been studied in recent literature. A key ingredient in the statistical modeling and analysis of testlet-based DCMs is the superposition of two latent structures, the attribute profile and the testlet effect. This paper extends the standard testlet DINA (T-DINA) model to accommodate the potential correlation between the two latent structures. Model identifiability is studied and a set of sufficient conditions are proposed. As a byproduct, the identifiability of the standard T-DINA is also established. The proposed model is applied to a dataset from the 2015 Programme for International Student Assessment. Comparisons are made with DINA and T-DINA, showing that there is substantial improvement in terms of the goodness of fit. Simulations are conducted to assess the performance of the new method under various settings.

Time limits are imposed on many computer-based assessments, and it is common to observe examinees who run out of time, resulting in missingness due to not-reached items. The present study proposes an approach to account for the missing mechanisms of not-reached items via response time censoring. The censoring mechanism is directly incorporated into the observed likelihood of item responses and response times. A marginal maximum likelihood estimator is proposed, and its asymptotic properties are established. The proposed method was evaluated and compared to several alternative approaches that ignore the censoring through simulation studies. An empirical study based on the PISA 2018 Science Test was further conducted.

A social network comprises both actors and the social connections among them. Such connections reflect the dependence among social actors, which is essential for individuals’ mental health and social development. In this article, we propose a mediation model with a social network as a mediator to investigate the potential mediation role of a social network. In the model, the dependence among actors is accounted for by a few mutually orthogonal latent dimensions which form a social space. The individuals’ positions in such a latent social space are directly involved in the mediation process between an independent and dependent variable. After showing that all the latent dimensions are equivalent in terms of their relationship to the social network and the meaning of each dimension is arbitrary, we propose to measure the whole mediation effect of a network. Although individuals’ positions in the latent space are not unique, we rigorously articulate that the proposed network mediation effect is still well defined. We use a Bayesian estimation method to estimate the model and evaluate its performance through an extensive simulation study under representative conditions. The usefulness of the network mediation model is demonstrated through an application to a college friendship network.

Computerized adaptive testing (CAT) is a sequential experiment design scheme that tailors the selection of experiments to each subject. Such a scheme measures subjects’ attributes (unknown parameters) more accurately than the regular prefixed design. In this paper, we consider CAT for diagnostic classification models, for which attribute estimation corresponds to a classification problem. After a review of existing methods, we propose an alternative criterion based on the asymptotic decay rate of the misclassification probabilities. The new criterion is then developed into new CAT algorithms, which are shown to achieve the asymptotically optimal misclassification rate. Simulation studies are conducted to compare the new approach with existing methods, demonstrating its effectiveness, even for moderate length tests.

Accurate assessment of a student’s ability is the key task of a test. Assessments based on final responses are the standard. As the infrastructure advances, substantially more information is observed. One of such instances is the process data that is collected by computer-based interactive items and contain a student’s detailed interactive processes. In this paper, we show both theoretically and with simulated and empirical data that appropriately including such information in the assessment will substantially improve relevant assessment precision.

Process data refer to data recorded in log files of computer-based items. These data, represented as timestamped action sequences, keep track of respondents’ response problem-solving behaviors. Process data analysis aims at enhancing educational assessment accuracy and serving other assessment purposes by utilizing the rich information contained in response processes. The R package ProcData presented in this article is designed to provide tools for inspecting, processing, and analyzing process data. We define an S3 class ‘proc’ for organizing process data and extend generic methods summary and print for ‘proc’. Feature extraction methods for process data are implemented in the package for compressing information in the irregular response processes into regular numeric vectors. ProcData also provides functions for making predictions from neural-network-based sequence models. In addition, a real dataset of response processes from the climate control item in the 2012 Programme for International Student Assessment is included in the package.

This paper studies changes of standard errors (SE) of the normal-distribution-based maximum likelihood estimates (MLE) for confirmatory factor models as model parameters vary. Using logical analysis, simplified formulas and numerical verification, monotonic relationships between SEs and factor loadings as well as unique variances are found. Conditions under which monotonic relationships do not exist are also identified. Such functional relationships allow researchers to better understand the problem when significant factor loading estimates are expected but not obtained, and vice versa. What will affect the likelihood for Heywood cases (negative unique variance estimates) is also explicit through these relationships. Empirical findings in the literature are discussed using the obtained results.

Our study aimed to develop and validate a nomogram to assess talaromycosis risk in hospitalized HIV-positive patients. Prediction models were built using data from a multicentre retrospective cohort study in China. On the basis of the inclusion and exclusion criteria, we collected data from 1564 hospitalized HIV-positive patients in four hospitals from 2010 to 2019. Inpatients were randomly assigned to the training or validation group at a 7:3 ratio. To identify the potential risk factors for talaromycosis in HIV-infected patients, univariate and multivariate logistic regression analyses were conducted. Through multivariate logistic regression, we determined ten variables that were independent risk factors for talaromycosis in HIV-infected individuals. A nomogram was developed following the findings of the multivariate logistic regression analysis. For user convenience, a web-based nomogram calculator was also created. The nomogram demonstrated excellent discrimination in both the training and validation groups [area under the ROC curve (AUC) = 0.883 vs. 0.889] and good calibration. The results of the clinical impact curve (CIC) analysis and decision curve analysis (DCA) confirmed the clinical utility of the model. Clinicians will benefit from this simple, practical, and quantitative strategy to predict talaromycosis risk in HIV-infected patients and can implement appropriate interventions accordingly.

We demonstrated a method to improve the output performance of a Ti:sapphire laser in the long-wavelength low-gain region with an efficient stimulated Raman scattering process. By shifting the wavelength of the high-gain-band Ti:sapphire laser to the long-wavelength low-gain region, high-performance Stokes operation was achieved in the original long-wavelength low-gain region of the Ti:sapphire laser. With the fundamental wavelength tuning from 870 to 930 nm, first-order Stokes output exceeding 2.5 W was obtained at 930–1000 nm, which was significantly higher than that directly generated by the Ti:sapphire laser, accompanied by better beam quality, shorter pulse duration and narrower linewidth. Under the pump power of 42.1 W, a maximum first-order Stokes power of 3.24 W was obtained at 960 nm, with a conversion efficiency of 7.7%. Furthermore, self-mode-locked modulations of first- and second-order Stokes generation were observed in Ti:sapphire intracavity solid Raman lasers for the first time.

The incidence of obesity-related glomerulopathy (ORG) is rising worldwide with very limited treatment methods. Paralleled with the gut–kidney axis theory, the beneficial effects of butyrate, one of the short-chain fatty acids (SCFA) produced by gut microbiota, on metabolism and certain kidney diseases have gained growing attention. However, the effects of butyrate on ORG and its underlying mechanism are largely unexplored. In this study, a mice model of ORG was established with a high-fat diet feeding for 16 weeks, and sodium butyrate treatment was initiated at the 8th week. Podocyte injury, oxidative stress and mitochondria function were evaluated in mice kidney and validated in vitro in palmitic acid-treated-mouse podocyte cell lines. Further, the molecular mechanisms of butyrate on podocytes were explored. Compared with controls, sodium butyrate treatment alleviated kidney injuries and renal oxidative stress in high-fat diet-fed mice. In mouse podocyte cell lines, butyrate ameliorated palmitic acid-induced podocyte damage and helped maintain the structure and function of the mitochondria. Moreover, the effects of butyrate on podocytes were mediated via the GPR43-Sirt3 signal pathway, as evidenced by the diminished effects of butyrate with the intervention of GPR43 or Sirt3 inhibitors. In summary, we conclude that butyrate has therapeutic potential for the treatment of ORG. It attenuates high-fat diet-induced ORG and podocyte injuries through the activation of the GPR43-Sirt3 signalling pathway.

Predicting epidemic trends of coronavirus disease 2019 (COVID-19) remains a key public health concern globally today. However, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection rate in previous studies of the transmission dynamics model was mostly a fixed value. Therefore, we proposed a meta-Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) model by adding a time-varying SARS-CoV-2 reinfection rate to the transmission dynamics model to more accurately characterize the changes in the number of infected persons. The time-varying reinfection rate was estimated using random-effect multivariate meta-regression based on published literature reports of SARS-CoV-2 reinfection rates. The meta-SEIRS model was constructed to predict the epidemic trend of COVID-19 from February to December 2023 in Sichuan province. Finally, according to the online questionnaire survey, the SARS-CoV-2 infection rate at the end of December 2022 in Sichuan province was 82.45%. The time-varying effective reproduction number in Sichuan province had two peaks from July to December 2022, with a maximum peak value of about 15. The prediction results based on the meta-SEIRS model showed that the highest peak of the second wave of COVID-19 in Sichuan province would be in late May 2023. The number of new infections per day at the peak would be up to 2.6 million. We constructed a meta-SEIRS model to predict the epidemic trend of COVID-19 in Sichuan province, which was consistent with the trend of SARS-CoV-2 positivity in China. Therefore, a meta-SEIRS model parameterized based on evidence-based data can be more relevant to the actual situation and thus more accurately predict future trends in the number of infections.

Human alveolar echinococcosis is a hard-to-treat and largely untreated parasitic disease with high associated health care costs. The current antiparasitic treatment for alveolar echinococcosis relies exclusively on albendazole, which does not act parasiticidally and can induce severe adverse effects. Alternative, and most importantly, improved treatment options are urgently required. A drug repurposing strategy identified the approved antimalarial pyronaridine as a promising candidate against Echinococcus multilocularis infections. Following a 30-day oral regimen (80 mg kg−1 day−1), pyronaridine achieved an excellent therapeutic outcome in a clinically relevant hepatic alveolar echinococcosis murine model, showing a significant reduction in both metacestode size (72.0%) and counts (85.2%) compared to unmedicated infected mice, which revealed significantly more potent anti-echinococcal potency than albendazole treatment at an equal dose (metacestode size: 42.3%; counts: 4.1%). The strong parasiticidal activity of pyronaridine was further confirmed by the destructive damage to metacestode tissues observed morphologically. In addition, a screening campaign combined with computational similarity searching against an approved drug library led to the identification of pirenzepine, a gastric acid-inhibiting drug, exhibiting potent parasiticidal activity against protoscoleces and in vitro cultured small cysts, which warranted further in vivo investigation as a promising anti-echinococcal lead compound. Pyronaridine has a known drug profile and a long track record of safety, and its repurposing could translate rapidly to clinical use for human patients with alveolar echinococcosis as an alternative or salvage treatment.

We report on an experimental study of turbulent Rayleigh–Bénard convection with asymmetric top and bottom plates. The plates are covered with pyramid-shaped roughness elements whose aspect ratios are $\lambda =1$ or $\lambda =4$. In the low-Rayleigh-number regime ($Ra<1.9\times 10^9$), the heat transport efficiencies in the asymmetric cells, characterized by the Nusselt number, are smaller than those measured in a symmetric $\lambda = 1$ cell and are greater than those for a symmetric $\lambda = 4$ cell, whereas in the high-Rayleigh-number regime ($Ra>1.9\times 10^9$), the Nusselt numbers of the asymmetric cells are, in turn, greater than those for the symmetric cell with $\lambda = 1$ and smaller than those for the symmetric cell with $\lambda = 4$. In addition, the heat transports of individual plates are studied based on the temperature drops across both halves of the cell. In the low-$Ra$ regime, the $\lambda =1$ plate shows higher heat transfer than the $\lambda =4$ plate, while for the high-$Ra$ regime, the $\lambda =4$ plate shows a higher heat transport ability. In both regimes, the individual Nusselt number of the plate with lower heat transfer is insensitive to the topology of the other plate. Besides, it is found that the symmetry of the centre temperature distribution is robust to the symmetry breaking of boundary topographies. For the $Ra$ range explored, a weak temperature inversion is observed in the bulk of asymmetric rough cells. Finally, we remark that the temperature fluctuation at the cell centre and the Reynolds number associated with the large-scale circulation show universal power laws in terms of the flux Rayleigh number as $\sigma _{T_{c}}\sim Ra_F^{0.68}$ and $Re_{LSC}\sim Ra_F^{0.36}$, respectively.

This research communication screened and identified differentiated expressed genes in bovine mammary epithelial cells (BMECs) upon prolactin (PRL) stimulation. PRL of 5 μg/ml increased β-casein synthesis in BMECs with milk protein synthesis capacity. RNA sequencing (RNA-seq) was used to screen differentially expressed genes (DEGs). A total of 375 DEGs (165 up-regulated and 210 down-regulated) were identified between PRL-stimulated group and the control group. Gene ontology enrichment analysis showed that the up-regulated genes were primarily associated with cell functions, metabolic processes, and biological regulatory processes. Pathway enrichment analysis showed that the up-regulated genes were mainly enriched in JAK-STAT, Rap1, Ras and Notch signaling pathways, which are widely involved in cell proliferation, differentiation and milk component synthesis. This study provides an initial understanding of the changes in gene expression in BMECs with PRL-stimulation, as determined by RNA-seq transcriptomic analysis, thereby enhancing our knowledge of the molecular regulation of lactation metabolism.

In this paper, a high-level Green–Naghdi (HLGN) model for large-amplitude internal waves in a two-layer fluid system, where the upper-fluid layer is of finite depth and the lower-fluid layer is of infinite depth, is developed under the rigid-lid free-surface approximation. The equations of the present HLGN model follow Euler's equations under the sole assumption that the horizontal and vertical velocity distributions along the vertical column are presented by known shape functions for each layer. The linear dispersion relations of the HLGN model for different levels are presented and compared with those obtained by other strongly nonlinear models for deep water, including the fully nonlinear models that include the dispersion effects $O(\mu )$ (where $\mu$ is the ratio of the upper-fluid layer depth to a typical wavelength) derived by Choi & Camassa (Phys. Rev. Lett., vol. 77, 1996, pp. 1759–1762) and $O(\mu ^2)$ derived by Debsarma et al. (J. Fluid Mech., vol. 654, 2010, pp. 281–303). It is shown that the HLGN model has a wider application range than other models. Solutions of travelling large-amplitude internal solitary waves in the absence and presence of background shear-current are then investigated by using the HLGN model. For the no-current cases, results obtained by the HLGN model show better agreement with Euler's solution on wave profile, velocity profile at the maximum interface displacement and wave speed compared with those obtained by other models. For the background shear-current cases, results obtained by the HLGN model also show good agreement with those obtained by solving the Dubreil-Jacotin–Long equation.