Natural enemies serve a crucial role in crop protection through the regulation of pest population dynamics. Cyrtorhinus lividipennis is an important natural enemy of rice planthoppers. Fatty acid synthase (FAS), a multifunctional enzyme crucial for fatty acid biosynthesis, serves as a vital energy source for insect reproduction. However, the function of FAS in the reproductive processes of C. lividipennis remains incompletely understood. In this study, the ClFAS gene was successfully cloned from C. lividipennis. The open reading frame of ClFAS was 7224 bp, encoding a putative protein of 2407 amino acids. The expression levels of ClFAS were notably elevated in the fifth-instar nymphs, adults, as well as in the fat body and ovaries of female individuals. Silencing of ClFAS resulted in a reduction of 58.4%, 34.6%, and 49.0% in the expression levels of ClVg at 1-, 2-, and 3-days post-dsRNA injection, respectively. Furthermore, RNA interference (RNAi)-mediated depletion of ClFAS not only suppressed the Vg protein expression but also significantly impaired oocyte maturation and ovarian development. The fecundity of dsFAS-treated C. lividipennis females was markedly reduced by 49.5%, accompanied by significant decreases of 32.7% in oviposition duration and 26.3% in female adult lifespan. Our findings showed that ClFAS positively regulates the reproduction of C. lividipennis by promoting vitellogenesis and ovarian development, which provides valuable insights into how lipid metabolism governs fecundity in predatory insects.

Within the broad context of design research, joint attention within co-creation represents a critical component, linking cognitive actors through dynamic interactions. This study introduces a novel approach employing deep learning algorithms to objectively quantify joint attention, offering a significant advancement over traditional subjective methods. We developed an optimized deep learning algorithm, YOLO-TP, to identify participants’ engagement in design workshops accurately. Our research methodology involved video recording of design workshops and subsequent analysis using the YOLO-TP algorithm to track and measure joint attention instances. Key findings demonstrate that the algorithm effectively quantifies joint attention with high reliability and correlates well with known measures of intersubjectivity and co-creation effectiveness. This approach not only provides a more objective measure of joint attention but also allows for the real-time analysis of collaborative interactions. The implications of this study are profound, suggesting that the integration of automated human activity recognition in co-creation can significantly enhance the understanding and facilitation of collaborative design processes, potentially leading to more effective design outcomes.

Data on the distribution of iodine in the urine and breast milk of lactating women are limited. This study aimed to establish a formula to assess iodine status in lactating women by evaluating the fractional iodine excretion in urine and breast milk. A 3-d 24-h iodine metabolism survey in 2021–2023 was conducted on fifty-four pairs of lactating women and infants in Tianjin and Luoyang, China. We used the 24-h dietary record and salt weighing method to assess daily iodine intake (DII). Iodine excretion in breast milk and urine was measured. The median 24-h urinary iodine concentration and breast milk iodine concentration were 135·06 μg/L and 150·26 µg/L, respectively. When the DII was between 240 μg/d and 600 μg/d, the predicted value of fractional breast milk iodine excretion was 31·48 % (95 % CI: 27·16 %, 36·22 %). When the daily iodine excretion was between 258 μg/d and 476 μg/d, the fractional urine iodine excretion (59·09 %) and fractional breast milk iodine excretion (40·91 %) were stable. DII can be derived from the spot urinary iodine concentration as follows: urinary iodine concentration (μg/L) × (0·0009 L/h/kg × 24 h/d) × body weight (kg) ÷ 0·59 ÷ 0·94 = DII (μg/d). In conclusion, lactating women with adequate iodine delivered approximately 31·48 % of the DII to their infants. A stable proportion (59·09 %) of iodine excretion was discharged through urine, which was used to assess the iodine status based on the spot urinary iodine concentration of lactating women. This study was registered at ClinicalTrials.gov (NCT04492657).

Extant studies on cross-border venture capital (VC) investment predominantly focus on how country-level formal institutions impact the flow of VCs across borders, but the potential role of country-level sentiments in this process has received less attention. Drawing upon the trust literature, we explore how home country political sentiment affects cross-border VC investment. Using data on Chinese VCs’ cross-border investments from 2000 to 2021, we find that home country political sentiment positively affects the amount of cross-border VC investment. Government VC (GVC) and connected VC (through sentiment transmission) positively, while investor managerial team education and investor host country experience (through sentiment suppression) negatively, moderate the influence of home country political sentiment.

Femoral neck bone mineral density (FNBMD) is a high risk factor for femoral head fractures, and coffee intake affects bone mineral density, but the effect on FNBMD remains to be explored. First, we conducted an observational study in the National Health and Nutrition Examination Survey and collected data on coffee intake, FNBMD, and sixteen covariates. Weight linear regression was used to explore the association of coffee intake with FNBMD. Then, Mendelian randomisation (MR) was used to explore the causal relationship between coffee intake and FNBMD, the exposure factor was coffee intake, and the outcome factor was FNBMD. The inverse variance weighting (IVW) method was used for the analysis, while heterogeneity tests, sensitivity, and pleiotropy analysis were performed. A total of 5 915 people were included in the cross-sectional study, including 3 178 men and 2 737 women. In the completely adjusted model, no coffee was used as a reference. The ORs for the overall population at ‘< 1’, ‘1–<2’, ‘2–<4’, and ‘4+’ (95% CI) were 0.02 (–0.01, 0.04), 0.00 (–0.01, 0.02), –0.01 (–0.02, 0.00), and 0.00 (–0.01, 0.02), respectively. The male and female population showed no statistically significant differences in both univariate and multivariate linear regressions. In the MR study, the IVW results showed an OR (95% CI) of 1.06 (0.88–1.27), a P-value of 0.55, and an overall F-value of 80.31. The heterogeneity, sensitivity analyses, and pleiotropy had no statistical significance. Our study used cross-sectional studies and MR to demonstrate that there is no correlation or causal relationship between coffee intake and FNBMD.

Insufficient sleep’s impact on cognitive and emotional function is well-documented, but its effects on social functioning remain understudied. This research investigates the influence of depressive symptoms on the relationship between sleep deprivation (SD) and social decision-making. Forty-two young adults were randomly assigned to either the SD or sleep control (SC) group. The SD group stayed awake in the laboratory, while the SC group had a normal night’s sleep at home. During the subsequent morning, participants completed a Trust Game (TG) in which a higher monetary offer distributed by them indicated more trust toward their partners. They also completed an Ultimatum Game (UG) in which a higher acceptance rate indicated more rational decision-making. The results revealed that depressive symptoms significantly moderated the effect of SD on trust in the TG. However, there was no interaction between group and depressive symptoms found in predicting acceptance rates in the UG. This study demonstrates that individuals with higher levels of depressive symptoms display less trust after SD, highlighting the role of depressive symptoms in modulating the impact of SD on social decision-making. Future research should explore sleep-related interventions targeting the psychosocial dysfunctions of individuals with depression.

Schistosomiasis is a parasitic disease that imposes a significant burden on society. The eggs are the primary pathogenic factor in schistosomiasis, and their accumulation in liver could lead to the formation of granulomas and liver fibrosis. However, the metabolic changes in liver resulting from schistosomiasis remain poorly understood. We established a mouse model of schistosomiasis japonica, where the eggs accumulate in the liver and form egg granulomas. We used mass spectrometry imaging to analyze the differences in metabolites among various liver regions, including the liver tissue from normal mice, the liver area outside the granulomas in schistosomiasis mice, and the granuloma region in schistosomiasis mice. There were significant differences in metabolites between different liver regions, which enriched in metabolic pathways such as the biosynthesis of unsaturated fatty acids, taurine and hypotaurine metabolism, glycerophospholipid metabolism, glycolysis/gluconeogenesis, purine metabolism, arachidonic acid metabolism, and bile secretion. In normal liver tissue, higher concentrations of oleic acid (FA (18:1)), eicosapentaenoic acid (FA (20:5)), and L-glutamine were observed. In liver regions outside the granulomas, D-glucose and pyruvic acid were elevated compared to those in normal mice. Taurine increased in the liver of schistosomiasis. Meanwhile, there were elevated uric acid and spermidine in the egg granulomas. We employed mass spectrometry imaging technology to investigate metabolic reprogramming in liver of Schistosoma japonicum-infected mice. We explored the spatial distribution of differential metabolites in liver of schistosomiasis including unsaturated fatty acids, taurine, glutamine, spermidine, and uric acid. Our research provides valuable insights for further elucidating metabolic reprogramming in schistosomiasis.

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.