Diagnostic classification models are confirmatory in the sense that the relationship between the latent attributes and responses to items is specified or parameterized. Such models are readily interpretable with each component of the model usually having a practical meaning. However, parameterized diagnostic classification models are sometimes too simple to capture all the data patterns, resulting in significant model lack of fit. In this paper, we attempt to obtain a compromise between interpretability and goodness of fit by regularizing a latent class model. Our approach starts with minimal assumptions on the data structure, followed by suitable regularization to reduce complexity, so that readily interpretable, yet flexible model is obtained. An expectation–maximization-type algorithm is developed for efficient computation. It is shown that the proposed approach enjoys good theoretical properties. Results from simulation studies and a real application are presented.

Item response theory (IRT) plays an important role in psychological and educational measurement. Unlike the classical testing theory, IRT models aggregate the item level information, yielding more accurate measurements. Most IRT models assume local independence, an assumption not likely to be satisfied in practice, especially when the number of items is large. Results in the literature and simulation studies in this paper reveal that misspecifying the local independence assumption may result in inaccurate measurements and differential item functioning. To provide more robust measurements, we propose an integrated approach by adding a graphical component to a multidimensional IRT model that can offset the effect of unknown local dependence. The new model contains a confirmatory latent variable component, which measures the targeted latent traits, and a graphical component, which captures the local dependence. An efficient proximal algorithm is proposed for the parameter estimation and structure learning of the local dependence. This approach can substantially improve the measurement, given no prior information on the local dependence structure. The model can be applied to measure both a unidimensional latent trait and multidimensional latent traits.

The eddy-viscosity model, as initially used to model the mean Reynolds stress, has been widely used in the linear analysis of turbulence recently by direct extension. In this study, the mechanism of the eddy viscosity in improving the prediction of fluctuation structures with linear analysis is clarified by investigating the statistical properties of forcing, eddy-viscosity term and their correlations. From the direct numerical simulation (DNS) results of turbulent channel flows with $Re_{\tau }=186$–$2003$, the spatial correlation of forcing is partially cancelled due to its interaction with eddy-viscosity terms. The stochastic forcing after excluding the eddy-viscosity term is nearly uncorrelated spatially, which better matches the condition where the resolvent modes are consistent with the spectral proper orthogonal decomposition (SPOD) modes from DNS. With the above findings, an optimisation framework is developed by minimising the spatial correlations of the stochastic forcing. The optimised eddy-viscosity profiles nearly overlap with the mean-quantity-based model in the near-wall region, but have different maximum values. Compared with the mean-quantity-based model, the optimised results enhance the consistency between the resolvent and DNS results significantly. Based on the optimised results, a new modelling framework is further abstracted, leaving only one to-be-modelled parameter of the maximum value of the eddy-viscosity profile. This parameter follows distinctive rules with spanwise flow scales, based on which a simplified predictive model is constructed. The resolvent modes predicted by the new model exhibit high consistency with SPOD modes, which are essentially comparable to the optimised results for wide ranges of streamwise and spanwise scales.

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.

The maser instability associated with the loss-cone distribution has been widely invoked to explain the radio bursts observed in the astrophysical plasma environment, such as aurora and corona. In the laboratory plasma of a tokamak, events reminiscent of these radio bursts have also been frequently observed as an electron cyclotron emission (ECE) burst in the microwave range ($\mathrm{\sim }2{f_{\textrm{ce}}}$ near the last closed flux surface) during transient magnetohydrodynamic events. These bursts have a short duration of ~10 μs and display a radiation spectrum corresponding to a radiation temperature ${T_{e,\textrm{rad}}}$ of over $30\ \textrm{keV}$ while the edge thermal electron temperature ${T_e}$ is only in the range of $1\ \textrm{keV}$. Suprathermal electrons can be generated through magnetic reconnection, and a loss-cone distribution can be generated through open stochastic field lines in the magnetic mirror of the near-edge region of a tokamak plasma. Radiation modelling shows that a sharp distribution gradient $\partial f/\partial {v_ \bot } > 0$ at the loss-cone boundary can cause a negative absorption of ECE radiation through the maser instability. The negative absorption then amplifies the radiation so that the microwave intensity is significantly stronger than the thermal value. The significant ${T_{e,\textrm{rad}}}$ from the simulations suggests the potential role of the loss-cone maser instability in generating the ECE burst in a tokamak.

This study aims to evaluate the impact of low-carbohydrate diet, balanced dietary guidance and pharmacotherapy on weight loss among individuals with overweight or obesity over a period of 3 months. The study involves 339 individuals with overweight or obesity and received weight loss treatment at the Department of Clinical Nutrition at the Second Affiliated Hospital of Zhejiang University, School of Medicine, between 1 January 2020 and 31 December 2023. The primary outcome is the percentage weight loss. Among the studied patients, the majority chose low-carbohydrate diet as their primary treatment (168 (49·56 %)), followed by balanced dietary guidance (139 (41·00 %)) and pharmacotherapy (32 (9·44 %)). The total percentage weight loss for patients who were followed up for 1 month, 2 months and 3 months was 4·98 (3·04, 6·29) %, 7·93 (5·42, 7·93) % and 10·71 (7·74, 13·83) %, respectively. Multivariable logistic regression analysis identified low-carbohydrate diet as an independent factor associated with percentage weight loss of ≥ 3 % and ≥ 5 % at 1 month (OR = 0·461, P < 0·05; OR = 0·349, P < 0·001). The results showed that a low-carbohydrate diet was an effective weight loss strategy in the short term. However, its long-term effects were comparable to those observed with balanced dietary guidance and pharmacotherapy.

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.

Environmental exposures are known to be associated with pathogen transmission and immune impairment, but the association of exposures with aetiology and severity of community-acquired pneumonia (CAP) are unclear. A retrospective observational study was conducted at nine hospitals in eight provinces in China from 2014 to 2019. CAP patients were recruited according to inclusion criteria, and respiratory samples were screened for 33 respiratory pathogens using molecular test methods. Sociodemographic, environmental and clinical factors were used to analyze the association with pathogen detection and disease severity by logistic regression models combined with distributed lag nonlinear models. A total of 3323 CAP patients were included, with 709 (21.3%) having severe illness. 2064 (62.1%) patients were positive for at least one pathogen. More severe patients were found in positive group. After adjusting for confounders, particulate matter (PM) 2.5 and 8-h ozone (O3-8h) were significant association at specific lag periods with detection of influenza viruses and Klebsiella pneumoniae respectively. PM10 and carbon monoxide (CO) showed cumulative effect with severe CAP. Pollutants exposures, especially PM, O3-8h, and CO should be considered in pathogen detection and severity of CAP to improve the clinical aetiological and disease severity diagnosis.

China faces challenges in meeting the World Health Organization (WHO)’s target of reducing hepatitis B virus (HBV) infections by 95% using 2015 as the baseline. Using Global Burden of Disease (GBD) 2019 data, joinpoint regression models were used to analyse the temporal trends in the crude incidence rates (CIRs) and age-standardized incidence rates (ASIRs) of acute HBV (AHBV) infections in China from 1990 to 2019. The age–period–cohort model was used to estimate the effects of age, period, and birth cohort on AHBV infection risk, while the Bayesian age–period–cohort (BAPC) model was applied to predict the annual number and ASIRs of AHBV infections in China through 2030. The joinpoint regression model revealed that CIRs and ASIRs decreased from 1990 to 2019, with a faster decline occurring among males and females younger than 20 years. According to the age–period–cohort model, age effects showed a steep increase followed by a gradual decline, whereas period effects showed a linear decline, and cohort effects showed a gradual rise followed by a rapid decline. The number of cases of AHBV infections in China was predicted to decline until 2030, but it is unlikely to meet the WHO’s target. These findings provide scientific support and guidance for hepatitis B prevention and control.

Here, we report the generation of MeV alpha-particles from H-11B fusion initiated by laser-accelerated boron ions. Boron ions with maximum energy of 6 MeV and fluence of 109/MeV/sr@5 MeV were generated from 60 nm-thick self-supporting boron nanofoils irradiated by 1 J femtosecond pulses at an intensity of 1019 W/cm2. By bombarding secondary hydrogenous targets with the boron ions, 3 × 105/sr alpha-particles from H-11B fusion were registered, which is consistent with the theoretical yield calculated from the measured boron energy spectra. Our results demonstrated an alternative way toward ultrashort MeV alpha-particle sources employing compact femtosecond lasers. The ion acceleration and product measurement scheme are referential for the studies on the ion stopping power and cross section of the H-11B reaction in solid or plasma.

The objective of this study was to understand and measure epigenetic changes associated with the occurrence of CHDs by utilizing the discordant monozygotic twin model. A unique set of monozygotic twins discordant for double-outlet right ventricles (DORVs) was used for this multiomics study. The cardiac and muscle tissue samples from the twins were subjected to whole genome sequencing, whole genome bisulfite sequencing, RNA-sequencing and liquid chromatography-tandem mass spectrometry analysis. Sporadic DORV cases and control fetuses were used for validation. Global hypomethylation status was observed in heart tissue samples from the affected twins. Among 36,228 differentially methylated regions (DMRs), 1097 DMRs involving 1039 genes were located in promoter regions. A total of 419 genes, and lncRNA–mRNA pairs involved 30 genes, and 62 proteins were significantly differentially expressed. Multiple omics integrative analysis revealed that five genes, including BGN, COL1A1, COL3A1, FBLN5, and FLAN, and three pathways, including ECM-receptor interaction, focal adhesion and TGF-β signaling pathway, exhibited differences at all three levels. This study demonstrates a multiomics profile of discordant twins and explores the possible mechanism of DORV development. Global hypomethylation might be associated with the risk of CHDs. Specific genes and specific pathways, particularly those involving ECM–receptor interaction, focal adhesion and TGF–β signaling, might be involved in the occurrence of CHDs.

Longitudinal studies on the variations of phenotypic and genotypic characteristics of K. pneumoniae across two decades are rare. We aimed to determine the antimicrobial susceptibility and virulence factors for K. pneumoniae isolated from patients with bacteraemia or urinary tract infection (UTI) from 1999 to 2022. A total of 699 and 1,267 K. pneumoniae isolates were isolated from bacteraemia and UTI patients, respectively, and their susceptibility to twenty antibiotics was determined; PCR was used to identify capsular serotypes and virulence-associated genes. K64 and K1 serotypes were most frequently observed in UTI and bacteraemia, respectively, with an increasing frequency of K20, K47, and K64 observed in recent years. entB and wabG predominated across all isolates and serotypes; the least frequent virulence gene was htrA. Most isolates were susceptible to carbapenems, amikacin, tigecycline, and colistin, with the exception of K20, K47, and K64 where resistance was widespread. The highest average number of virulence genes was observed in K1, followed by K2, K20, and K5 isolates, which suggest their contribution to the high virulence of K1. In conclusion, we found that the distribution of antimicrobial susceptibility, virulence gene profiles, and capsular types of K. pneumoniae over two decades were associated with their clinical source.

My 5 moments (M5M) was used less frequently among cleaning staff members, suggesting that a poor compliance score in this group may not indicate deficient handwashing. This quasi-experimental study compared hand hygiene compliance (HHC), hand hygiene (HH) moments, and HH time distribution in the control group (no HH intervention; n = 21), case group 1 (normal M5M intervention; n = 26), case group 2 (extensive novel six moments (NSM) training; n = 24), and case group 3 (refined NSM training; n = 18). The intervention’s effect was evaluated after 3 months. The HHC gap among the four groups gradually increased in the second intervention month (control group, 31.43%; case group 1, 38.74%; case group 2, 40.19%; case group 3, 52.21%; p < 0.05). After the intervention period, the HHC of case groups 2 and 3 improved significantly from the baseline (23.85% vs. 59.22%, 27.41% vs. 83.62%, respectively; p < 0.05). ‘After transferring medical waste from the site’ had the highest HHC in case group 3, 90.72% (95% confidence interval, 0.1926–0.3967). HH peak hours were from 6 AM to 9 AM and 2 PM to 3 PM. The study showed that the implementation of an NSM practice can serve as an HHC monitoring indicator and direct relevant training interventions to improve HH among hospital cleaning staff.

Trematodes of the genus Ogmocotyle are intestinal flukes that can infect a variety of definitive hosts, resulting in significant economic losses worldwide. However, there are few studies on molecular data of these trematodes. In this study, the mitochondrial (mt) genome of Ogmocotyle ailuri isolated from red panda (Ailurus fulgens) was determined and compared with those from Pronocephalata to investigate the mt genome content, genetic distance, gene rearrangements and phylogeny. The complete mt genome of O. ailuri is a typical closed circular molecule of 14 642 base pairs, comprising 12 protein-coding genes (PCGs), 22 transfer RNA genes, 2 ribosomal RNA genes and 2 non-coding regions. All genes are transcribed in the same direction. In addition, 23 intergenic spacers and 2 locations with gene overlaps were determined. Sequence identities and sliding window analysis indicated that cox1 is the most conserved gene among 12 PCGs in O. ailuri mt genome. The sequenced mt genomes of the 48 Plagiorchiida trematodes showed 5 types of gene arrangement based on all mt genome genes, with the gene arrangement of O. ailuri being type I. Phylogenetic analysis using concatenated amino acid sequences of 12 PCGs revealed that O. ailuri was closer to Ogmocotyle sikae than to Notocotylus intestinalis. These data enhance the Ogmocotyle mt genome database and provide molecular resources for further studies of Pronocephalata taxonomy, population genetics and systematics.