Cryphodera guangdongensis n. sp. was collected from the soil and roots of Schima superba in Guangdong province, China. The new species is characterised by having a nearly spherical female, with dimensions of length × width = 532.3 (423.8–675.3) × 295.6 (160.0–381.2) μm, stylet length of 35.7 (31.1–42.1) μm, protruding vulval lips, a vulval slit measuring 54.2 (47.4–58.9) μm, an area between the vulva and anus that is flat to concave, and a vulva–anus distance 49.3 (41.1–57.6) μm. The male features two lip annules, a stylet length of 31.7 (27.4–34.8) μm and basal knobs that are slightly projecting anteriorly, while lateral field is areolated with three incisures and spicules length of 27.1 (23.7–31.0) μm. The second stage juvenile is characterised by a body length of 506.1 (441.8–564.4) μm long, two to three lip annules, a stylet length 31.2 (29.7–33.2) μm which is well developed, basal knobs projecting anteriorly, a lateral field that is areolate with three incisures, and a narrow rounded tail measuring 63.2 (54.2–71.3) μm long, with a hyaline region of 35.6 (27.4–56.6) μm long that is longer than the stylet. Based on morphology and morphometrics, the new species is closely related to C. sinensis and C. japonicum within the genus Cryphodera. The phylogenetic trees constructed based on the ITS-rRNA, 28S-rRNA D2–D3 region, and the partial COI gene sequences indicate that the new species clusters with other Cryphodera species but maintains in a separated subgroup. A key to the species of the genus Cryphodera is also provided in this study.

Previous studies have reported comorbidities of autoimmune thyroid disorders (AITD), including Hashimoto’s disease (HT) and Graves’ disease (GD), and celiac disease (CeD), as well as the possible beneficial effects of a gluten-free diet (GFD) on AITD. Nonetheless, it remains uncertain whether there is a genetic causal relationship between AITD and CeD, while the beneficial effects on a GFD are controversial. This study aim to explore the causal relationship between CeD and AITD, particularly with HT, and to determine whether a GFD is beneficial for AITD. We performed a two-sample Mendelian Randomization (MR) analysis on data from the largest meta-analysis summary statistics of AITD, CeD and GFD. Genetic instrumental variables were established by pinpointing single nucleotide polymorphisms (SNPs) that relate to corresponding factors. In assessing sensitivity and heterogeneity, we conducted examinations of MR Egger, weighted median, simple mode, weighted mode, and MR Egger intercept tests. HT was found to play a pathogenic role in increasing the risk of CeD (ORIVW = 1.544 [95%CI 1.153-2.068], p = 0.00355), and our Mendelian randomization study does not support genetic liability related to CeD with GD (Graves’ disease) and GFD with AITD. This study supports the positive correlation between HT risk and CeD risk, while GFD has no protective effect on AITD and may exert its effect through other mechanisms. These findings provide valuable insights into potential targets for disease intervention and treatment at the genetic level.

Milk fat is a crucial component for evaluating the production performance and nutritional value of goat milk. Previous research indicated that the composition of ruminal microbiota plays a significant role in regulating milk fat percentage in ruminants. Thus, this study aimed to identify key ruminal microorganisms and blood metabolites relevant to milk fat synthesis in dairy goats as a mean to explore their role in regulating milk fat synthesis. Sixty clinically healthy Xinong Saanen dairy goats at mid-lactation and of similar body weight, and similar milk yield were used in a feeding study for 15 days. Based on daily milk yield of dairy goats and the results of milk component determination on the 1st and 8th days, five goats with the highest milk fat content (H group) and five goats with the lowest milk fat content (L group) were selected for further analysis. Before the morning feeding on the 15th day of the experiment, samples of milk, blood and ruminal fluid were collected for analyses of components, volatile fatty acids, microbiota and metabolites. Results revealed that acetate content in the rumen of H group was greater compared with L group. H group had abundant beneficial bacteria including Ruminococcaceae_UCG-005, Saccharofermentans, Ruminococcaceae-UCG-002 and Prevotellaceae_UCG-3, which were important for plant cellulose and hemicellulose degradation and immune regulation. Metabolomics analysis revealed H group had greater relative concentrations of 4-acetamidobutanoic acid and azelaic acid in serum, and had lower relative concentrations of Arginyl-Alanine, SM(d18:1/12:0) and DL-Tryptophan. These altered metabolites are involved in the sphingolipid signaling pathway, arginine and proline metabolism. Overall, this study identified key ruminal microorganisms and serum metabolites associated with milk fat synthesis in dairy goats. These findings offer insights for enhancing the quality of goat milk and contribute to a better understanding of the regulatory mechanisms involved in milk fat synthesis in dairy goats.

Fitting propensity (FP) analysis quantifies model complexity but has been impeded in item response theory (IRT) due to the computational infeasibility of uniformly and randomly sampling multinomial item response patterns under a full-information approach. We adopt a limited-information (LI) approach, wherein we generate data only up to the lower-order margins of the complete item response patterns. We present an algorithm that builds upon classical work on sampling contingency tables with fixed margins by implementing a Sequential Importance Sampling algorithm to Quickly and Uniformly Obtain Contingency tables (SISQUOC). Theoretical justification and comprehensive validation demonstrate the effectiveness of the SISQUOC algorithm for IRT and offer insights into sampling from the complete data space defined by the lower-order margins. We highlight the efficiency and simplicity of the LI approach for generating large and uniformly random datasets of dichotomous and polytomous items. We further present an iterative proportional fitting procedure to reconstruct joint multinomial probabilities after LI-based data generation, facilitating FP evaluation using traditional estimation strategies. We illustrate the proposed approach by examining the FP of the graded response model and generalized partial credit model, with results suggesting that their functional forms express similar degrees of configural complexity.

The underwater target detection is affected by image blurring caused by suspended particles in water bodies and light scattering effects. To tackle this issue, this paper proposes a reparameterized feature enhancement and fusion network for underwater blur object recognition (REFNet). First, this paper proposes the reparameterized feature enhancement and gathering (REG) module, which is designed to enhance the performance of the backbone network. This module integrates the concepts of reparameterization and global response normalization to enhance the network’s feature extraction capabilities, addressing the challenge of feature extraction posed by image blurriness. Next, this paper proposes the cross-channel information fusion (CIF) module to enhance the neck network. This module combines detailed information from shallow features with semantic information from deeper layers, mitigating the loss of image detail caused by blurring. Additionally, this paper replace the CIoU loss function with the Shape-IoU loss function improves target localization accuracy, addressing the difficulty in accurately locating bounding boxes in blurry images. Experimental results indicate that REFNet achieves superior performance compared to state-of-the-art methods, as evidenced by higher mAP scores on the underwater robot professional competitionand detection underwater objects datasets. REFNet surpasses YOLOv8 by approximately 1.5% in $mAP_{50:95}$ on the URPC dataset and by about 1.3% on the DUO dataset. This enhancement is achieved without significantly increasing the model’s parameters or computational load. This approach enhances the precision of target detection in challenging underwater environments.

A new fossil of Lycidae, Domipteron gaoi n. gen. n. sp., is described from Miocene Dominican amber. The fossil exhibits a combination of characteristics found in both Calopterini and Eurrhacini. To determine its systematic placement, we conducted phylogenetic analyses based on adult morphological features. Our analyses indicate that the new fossil belongs to Calopterini.

UUID: http://zoobank.org/d0f9a12e-aaed-48b0-a1dd-2c0991ef8b36

This paper presents an investigation of the secondary saturation characteristics of a HfTe2 saturable absorber. Pulse energies of 5.85 and 7.4 mJ were demonstrated with a high-order Hermite–Gaussian (HG) laser and a vortex laser, respectively, using alexandrite as the gain medium. To the best of our knowledge, these are the highest pulse energies directly generated with HG and vortex lasers. To broaden the applications of high-energy pulsed HG and vortex lasers, wavelength tuning in the region of 40 nm was achieved using an etalon.

The data volumes generated by theWidefield ASKAP L-band Legacy All-sky Blind surveY atomic hydrogen (Hi) survey using the Australian Square Kilometre Array Pathfinder (ASKAP) necessitate greater automation and reliable automation in the task of source finding and cataloguing. To this end, we introduce and explore a novel deep learning framework for detecting low signal-to-noise ratio (SNR) Hi sources in an automated fashion. Specifically, our proposed method provides an automated process for separating true Hi detections from false positives when used in combination with the source finding application output candidate catalogues. Leveraging the spatial and depth capabilities of 3D convolutional neural networks, our method is specifically designed to recognize patterns and features in three-dimensional space, making it uniquely suited for rejecting false-positive sources in low SNR scenarios generated by conventional linear methods. As a result, our approach is significantly more accurate in source detection and results in considerably fewer false detections compared to previous linear statistics-based source finding algorithms. Performance tests using mock galaxies injected into real ASKAP data cubes reveal our method’s capability to achieve near-100% completeness and reliability at a relatively low integrated SNR $\sim3-5$. An at-scale version of this tool will greatly maximise the science output from the upcoming widefield Hi surveys.

With decades of advance research and recent developments in the drug and medical device regulatory approval process, patient-reported outcomes (PROs) are becoming increasingly important in clinical trials. While clinical trial analyses typically treat scores from PROs as observed variables, the potential to use latent variable models when analyzing patient responses in clinical trial data presents novel opportunities for both psychometrics and regulatory science. An accessible overview of analyses commonly used to analyze longitudinal trial data and statistical models familiar in both psychometrics and biometrics, such as growth models, multilevel models, and latent variable models, is provided to call attention to connections and common themes among these models that have found use across many research areas. Additionally, examples using empirical data from a randomized clinical trial provide concrete demonstrations of the implementation of these models. The increasing availability of high-quality, psychometrically rigorous assessment instruments in clinical trials, of which the Patient-Reported Outcomes Measurement Information System (PROMIS®) is a prominent example, provides rare possibilities for psychometrics to help improve the statistical tools used in regulatory science.

Item response theory scoring based on summed scores is employed frequently in the practice of educational and psychological measurement. Lord and Wingersky (Appl Psychol Meas 8(4):453–461, 1984) proposed a recursive algorithm to compute the summed score likelihood. Cai (Psychometrika 80(2):535–559, 2015) extended the original Lord–Wingersky algorithm to the case of two-tier multidimensional item factor models and called it Lord–Wingersky algorithm Version 2.0. The 2.0 algorithm utilizes dimension reduction to efficiently compute summed score likelihoods associated with the general dimensions in the model. The output of the algorithm is useful for various purposes, for example, scoring, scale alignment, and model fit checking. In the research reported here, a further extension to the Lord–Wingersky algorithm 2.0 is proposed. The new algorithm, which we call Lord–Wingersky algorithm Version 2.5, yields the summed score likelihoods for all latent variables in the model conditional on observed score combinations. The proposed algorithm is illustrated with empirical data for three potential application areas: (a) describing achievement growth using score combinations across adjacent grades, (b) identification of noteworthy subscores for reporting, and (c) detection of aberrant responses.

Motivated by Gibbons et al.’s (Appl. Psychol. Meas. 31:4–19, 2007) full-information maximum marginal likelihood item bifactor analysis for polytomous data, and Rijmen, Vansteelandt, and De Boeck’s (Psychometrika 73:167–182, 2008) work on constructing computationally efficient estimation algorithms for latent variable models, a two-tier item factor analysis model is developed in this research. The modeling framework subsumes standard multidimensional IRT models, bifactor IRT models, and testlet response theory models as special cases. Features of the model lead to a reduction in the dimensionality of the latent variable space, and consequently significant computational savings. An EM algorithm for full-information maximum marginal likelihood estimation is developed. Simulations and real data demonstrations confirm the accuracy and efficiency of the proposed methods. Three real data sets from a large-scale educational assessment, a longitudinal public health survey, and a scale development study measuring patient reported quality of life outcomes are analyzed as illustrations of the model’s broad range of applicability.

A Metropolis–Hastings Robbins–Monro (MH-RM) algorithm for high-dimensional maximum marginal likelihood exploratory item factor analysis is proposed. The sequence of estimates from the MH-RM algorithm converges with probability one to the maximum likelihood solution. Details on the computer implementation of this algorithm are provided. The accuracy of the proposed algorithm is demonstrated with simulations. As an illustration, the proposed algorithm is applied to explore the factor structure underlying a new quality of life scale for children. It is shown that when the dimensionality is high, MH-RM has advantages over existing methods such as numerical quadrature based EM algorithm. Extensions of the algorithm to other modeling frameworks are discussed.

Lord and Wingersky’s (Appl Psychol Meas 8:453–461, 1984) recursive algorithm for creating summed score based likelihoods and posteriors has a proven track record in unidimensional item response theory (IRT) applications. Extending the recursive algorithm to handle multidimensionality is relatively simple, especially with fixed quadrature because the recursions can be defined on a grid formed by direct products of quadrature points. However, the increase in computational burden remains exponential in the number of dimensions, making the implementation of the recursive algorithm cumbersome for truly high-dimensional models. In this paper, a dimension reduction method that is specific to the Lord–Wingersky recursions is developed. This method can take advantage of the restrictions implied by hierarchical item factor models, e.g., the bifactor model, the testlet model, or the two-tier model, such that a version of the Lord–Wingersky recursive algorithm can operate on a dramatically reduced set of quadrature points. For instance, in a bifactor model, the dimension of integration is always equal to 2, regardless of the number of factors. The new algorithm not only provides an effective mechanism to produce summed score to IRT scaled score translation tables properly adjusted for residual dependence, but leads to new applications in test scoring, linking, and model fit checking as well. Simulated and empirical examples are used to illustrate the new applications.

We present a semi-parametric approach to estimating item response functions (IRF) useful when the true IRF does not strictly follow commonly used functions. Our approach replaces the linear predictor of the generalized partial credit model with a monotonic polynomial. The model includes the regular generalized partial credit model at the lowest order polynomial. Our approach extends Liang’s (A semi-parametric approach to estimate IRFs, Unpublished doctoral dissertation, 2007) method for dichotomous item responses to the case of polytomous data. Furthermore, item parameter estimation is implemented with maximum marginal likelihood using the Bock–Aitkin EM algorithm, thereby facilitating multiple group analyses useful in operational settings. Our approach is demonstrated on both educational and psychological data. We present simulation results comparing our approach to more standard IRF estimation approaches and other non-parametric and semi-parametric alternatives.

Foodborne diseases are ongoing and significant public health concerns. This study analysed data obtained from the Foodborne Outbreaks Surveillance System of Wenzhou to comprehensively summarise the characteristics of foodborne outbreaks from 2012 to 2022. A total of 198 outbreaks were reported, resulting in 2,216 cases, 208 hospitalisations, and eight deaths over 11 years. The findings suggested that foodborne outbreaks were more prevalent in the third quarter, with most cases occurring in households (30.8%). Outbreaks were primarily associated with aquatic products (17.7%) as sources of contamination. The primary transmission pathways were accidental ingestion (20.2%) and multi-pathway transmission (12.1%). Microbiological aetiologies (46.0%), including Vibrio parahaemolyticus, Salmonella ssp., and Staphylococcus aureus, were identified as the main causes of foodborne outbreaks. Furthermore, mushroom toxins (75.0%), poisonous animals (12.5%), and poisonous plants (12.5%) were responsible for deaths from accidental ingestion. This study identified crucial settings and aetiologies that require the attention of both individuals and governments, thereby enabling the development of effective preventive measures to mitigate foodborne outbreaks, particularly in coastal cities.

This study investigates the molecular intricacies of the transmembrane protein TSP11 gene in Echinococcus strains isolated from livestock and patients in Yunnan Province afflicted with Echinococcus granulosus (E. granulosus) between 2016 and 2020. Gene typing analysis of the ND1 gene revealed the presence of the G1 type, G5 type and untyped strains, constituting 52.4, 38.1 and 9.5%, respectively. The analysis of 42 DNA sequences has revealed 24 novel single nucleotide polymorphic sites, delineating 11 haplotypes, all of which were of the mutant type. Importantly, there were no variations observed in mutation sites or haplotypes in any of the hosts. The total length of the TSP11 gene's 4 exons is 762 bp, encoding 254 amino acids. Our analysis posits the existence of 6 potential B-cell antigenic epitopes within TSP11, specifically at positions 49-KSN-51, 139-GKRG-142, 162-DNG-164, 169-NGS-171, 185-DS-186 and 231-PPRFTN-236. Notably, these epitopes exhibit consistent presence among various intermediate hosts and haplotypes. However, further validation is imperative to ascertain their viability as diagnostic antigens for E. granulosus in the Yunnan Province.

Three new species of Gyrodactylus were identified from the body surface of the Triplophysa species from the Qinghai-Tibet Plateau, Gyrodactylus triplorienchili n. sp. on Triplophysa orientalis in northern Tibet, G. yellochili n. sp. on T. sellaefer and T. scleroptera and G. triplsellachili n. sp. on T. sellaefer and T. robusta in Lanzhou Reach of the Yellow River. The three newly identified species share the nemachili group species’ characteristic of having inturning hamulus roots. Gyrodactylus triplorienchili n. sp. shared a quadrate sickle heel and a thin marginal hook sickle, two morphological traits that set them apart from G. yellochili n. sp. However, they may be identified by the distinct shapes of the sickle base and marginal hook sickle point. Gyrodactylus triplsellachili n. sp. had much larger opisthaptoral hard part size than the other two species. The three new species show relatively low interspecific differences of 2.9–5.3% p-distance for ITS1-5.85-ITS2 rDNA sequences. Phylogenetic analysis indicated that the three new species formed a well-supported monophyletic group (bp = 99) with the other nemachili group species.