The global transition towards diets high in calories has contributed to 2.1 billion people becoming overweight, or obese, which damages male reproduction and harms offspring. Recently, more and more studies have shown that paternal exposure to stress closely affects the health of offspring in an intergenerational and transgenerational way. SET Domain Containing 2 (SETD2), a key epigenetic gene, is highly conserved among species, is a crucial methyltransferase for converting histone 3 lysine 36 dimethylation (H3K36me2) into histone 3 lysine 36 trimethylation (H3K36me3), and plays an important regulator in the response to stress. In this study, we compared patterns of SETD2 expression and the H3K36me3 pattern in pre-implantation embryos derived from normal or obese mice induced by high diet. The results showed that SETD2 mRNA was significantly higher in the high-fat diet (HFD) group than the control diet (CD) group at the 2-cell, 4-cell, 8-cell, and 16-cell stages, and at the morula and blastocyst stages. The relative levels of H3K36me3 in the HFD group at the 2-cell, 4-cell, 8-cell, 16-cell, morula stage, and blastocyst stage were significantly higher than in the CD group. These results indicated that dietary changes in parental generation (F0) male mice fed a HFD were traceable in SETD2/H3K36me3 in embryos, and that a paternal high-fat diet brings about adverse effects for offspring that might be related to SETD2/H3K36me3, which throws new light on the effect of paternal obesity on offspring from an epigenetic perspective.

Human gait trajectory prediction is a long-standing research topic in human–machine interaction. However, there are two shortcomings in the current gait trajectory prediction technology. The first shortcoming is that the neural network model of gait prediction only predicts dozens of future time frames of gait trajectory. The second shortcoming is that the gait prediction neural network model is uninterpretable. We propose the Interpretable-Concatenation former (IC-former) model, which can predict long-term gait trajectories and explain the prediction results by quantifying the importance of data at different positions in the input sequence. Experiments prove that the IC-former model we proposed not only makes a breakthrough in prediction accuracy but also successfully explains the data basis of the prediction.

Bidirectional transformations (BXs) are a mechanism for maintaining consistency between multiple representations of related data. The lens framework, which usually constructs BXs from lens combinators, has become the mainstream approach to BX programming because of its modularity and correctness by construction. However, the involved bidirectional behaviors of lenses make the equational reasoning and optimization of them much harder than unidirectional programs. We propose a novel approach to deriving efficient lenses from clear specifications via program calculation, a correct-by-construction approach to reasoning about functional programs by algebraic laws. To support bidirectional program calculation, we propose contract lenses, which extend conventional lenses with a pair of predicates to enable safe and modular composition of partial lenses. We define several contract-lens combinators capturing common computation patterns including $\textit{fold}, \textit{filter},\textit{map}$, and $\textit{scan}$, and develop several bidirectional calculation laws to reason about and optimize contract lenses. We demonstrate the effectiveness of our new calculation framework based on contract lenses with nontrivial examples.

The prevalence of workaholism has negative consequences on human health. Lack of sleep, a well-known problem among adults in modern society, is often attributed to overwork as a result of workaholism. Yet there is a lack of empirical research examining how and when workaholism will lead to sleep problems. To answer this question and to examine the longitudinal effect of workaholism on sleep in China, we investigate the mediating role of perceived evening responsibilities of work and the moderating effect of work autonomy. Two hundred and five Chinese working adults (58.0% female) voluntarily completed the online questionnaires at Time 1 (T1) and Time 2 (T2; 1-month later). Results showed that workaholism at T1 had a significant and positive correlation with sleep problem at T2. Further analysis suggested that perceived evening responsibilities of work fully mediated the relationship between workaholism and sleep problem. Work autonomy was shown to buffer the positive effect of workaholism on perceived evening responsibilities of work and attenuate the indirect effect of workaholism on sleep problem. While workers should be made aware of the negative impact of workaholism on sleep, organizations should also consider interventions to enhance employees’ autonomy and control of their work.

A multi-agent deep reinforcement learning (DRL)-based model is presented in this study to reconstruct flow fields from noisy data. A combination of reinforcement learning with pixel-wise rewards, physical constraints represented by the momentum equation and the pressure Poisson equation, and the known boundary conditions is used to build a physics-constrained deep reinforcement learning (PCDRL) model that can be trained without the target training data. In the PCDRL model, each agent corresponds to a point in the flow field and learns an optimal strategy for choosing pre-defined actions. The proposed model is efficient considering the visualisation of the action map and the interpretation of the model operation. The performance of the model is tested by using direct numerical simulation-based synthetic noisy data and experimental data obtained by particle image velocimetry. Qualitative and quantitative results show that the model can reconstruct the flow fields and reproduce the statistics and the spectral content with commendable accuracy. Furthermore, the dominant coherent structures of the flow fields can be recovered by the flow fields obtained from the model when they are analysed using proper orthogonal decomposition and dynamic mode decomposition. This study demonstrates that the combination of DRL-based models and the known physics of the flow fields can potentially help solve complex flow reconstruction problems, which can result in a remarkable reduction in the experimental and computational costs.

Maternal syphilis not only seriously affects the quality of life of pregnant women themselves but also may cause various adverse pregnancy outcomes (APOs). This study aimed to analyse the association between the related factors and APOs in maternal syphilis. 7,030 pregnant women infected with syphilis in Henan Province between January 2016 and December 2022 were selected as participants. Information on their demographic and clinical characteristics, treatment status, and pregnancy outcomes was collected. Multivariate logistic regression models and chi-squared automatic interaction detector (CHAID) decision tree models were used to analyse the factors associated with APOs. The multivariate logistic regression results showed that the syphilis infection history (OR = 1.207, 95% CI, 1.035–1.409), the occurrence of abnormality during pregnancy (OR = 5.001, 95% CI, 4.203–5.951), not receiving standard treatment (OR = 1.370, 95% CI, 1.095–1.716), not receiving any treatment (OR = 1.313, 95% CI, 1.105–1.559), and a titre ≥1:8 at diagnosis (OR = 1.350, 95%CI, 1.079–1.690) and before delivery (OR = 1.985, 95%CI, 1.463–2.694) were risk factors. A total of six influencing factors of APOs in syphilis-infected women were screened using the CHAID decision tree model. Integrated prevention measures such as early screening, scientific eugenics assessment, and standard syphilis treatment are of great significance in reducing the incidence of APOs for pregnant women infected with syphilis.

As the main global terrestrial ecosystem component, grasslands are extremely sensitive to global climate change. With increasing human activities over the last century, grassland ecosystems have been degraded to different degrees. However, the evolution of lake-grassland ecosystems in recent centuries remains unclear due to the dearth of high-resolution records. Here, we present high-resolution lacustrine sediment grain size, pollen (Artemisia, Myriophyllum), Pediastrum, and n-alkane records from Ganggeng Nur Lake to investigate vegetation, lake evolution, and human effects in semiarid northern China. Four stages were identified from the last ca. 150 years: (1) the natural evolution stage (AD 1870–1945), in which there was a wet climate around Ganggeng Nur and the lake level rose from increased runoff; (2) the human disturbance stage (AD 1945–1967), in which the regional climate got drier and human activities began having a detectable effect on the grassland ecosystem; (3) the human transformation stage (AD 1967–2005), in which a completely arid climate coupled with the implementation of a series of land reclamation policies resulted in a large reduction in grassland areas, extensive soil erosion, exacerbated climate change, and shrinking lake areas; and (4) the posttreatment stage (AD 2005–2018), in which soil erosion was alleviated by policy implementation and a favorable humid climate.

Shape memory polymers (SMPs) are a type of programmable materials capable of transforming their shapes in a pre-programmed way upon the application of an external stimulus. These materials have been tested for various potential applications particularly in the biomedical field for polymers with general and specific requirements. This review focuses on the recent advances in biomedical applications, including self-tightening sutures, pressure bandages, self-expansion stents, tissue engineering scaffolds, artificial muscles, drug delivery, and orthodontic archwires, after a brief description of the concepts, classifications, programming procedures, and material requirements of SMPs.

Coastal eutrophication and hypoxia remain a persistent environmental crisis despite the great efforts to reduce nutrient loading and mitigate associated environmental damages. Symptoms of this crisis have appeared to spread rapidly, reaching developing countries in Asia with emergences in Southern America and Africa. The pace of changes and the underlying drivers remain not so clear. To address the gap, we review the up-to-date status and mechanisms of eutrophication and hypoxia in global coastal oceans, upon which we examine the trajectories of changes over the 40 years or longer in six model coastal systems with varying socio-economic development statuses and different levels and histories of eutrophication. Although these coastal systems share common features of eutrophication, site-specific characteristics are also substantial, depending on the regional environmental setting and level of social-economic development along with policy implementation and management. Nevertheless, ecosystem recovery generally needs greater reduction in pressures compared to that initiated degradation and becomes less feasible to achieve past norms with a longer time anthropogenic pressures on the ecosystems. While the qualitative causality between drivers and consequences is well established, quantitative attribution of these drivers to eutrophication and hypoxia remains difficult especially when we consider the social economic drivers because the changes in coastal ecosystems are subject to multiple influences and the cause–effect relationship is often non-linear. Such relationships are further complicated by climate changes that have been accelerating over the past few decades. The knowledge gaps that limit our quantitative and mechanistic understanding of the human-coastal ocean nexus are identified, which is essential for science-based policy making. Recognizing lessons from past management practices, we advocate for a better, more efficient indexing system of coastal eutrophication and an advanced regional earth system modeling framework with optimal modules of human dimensions to facilitate the development and evaluation of effective policy and restoration actions.

This study proposes a newly developed deep-learning-based method to generate turbulent inflow conditions for spatially developing turbulent boundary layer (TBL) simulations. A combination of a transformer and a multiscale-enhanced super-resolution generative adversarial network is utilised to predict velocity fields of a spatially developing TBL at various planes normal to the streamwise direction. Datasets of direct numerical simulation (DNS) of flat plate flow spanning a momentum thickness-based Reynolds number, $Re_\theta = 661.5\unicode{x2013}1502.0$, are used to train and test the model. The model shows a remarkable ability to predict the instantaneous velocity fields with detailed fluctuations and reproduce the turbulence statistics as well as spatial and temporal spectra with commendable accuracy as compared with the DNS results. The proposed model also exhibits a reasonable accuracy for predicting velocity fields at Reynolds numbers that are not used in the training process. With the aid of transfer learning, the computational cost of the proposed model is considered to be effectively low. Furthermore, applying the generated turbulent inflow conditions to an inflow–outflow simulation reveals a negligible development distance for the TBL to reach the target statistics. The results demonstrate for the first time that transformer-based models can be efficient in predicting the dynamics of turbulent flows. They also show that combining these models with generative adversarial networks-based models can be useful in tackling various turbulence-related problems, including the development of efficient synthetic-turbulent inflow generators.

In this paper, we mainly study the function spaces related to H-sober spaces. For an irreducible subset system H and $T_{0}$ spaces X and Y, it is proved that the following three conditions are equivalent: (1) the Scott space $\Sigma \mathcal O(X)$ of the lattice of all open sets of X is H-sober; (2) for every H-sober space Y, the function space $\mathbb{C}(X, Y)$ of all continuous mappings from X to Y equipped with the Isbell topology is H-sober; (3) for every H-sober space Y, the Isbell topology on $\mathbb{C}(X, Y)$ has property S with respect to H. One immediate corollary is that for a $T_{0}$ space X, Y is a d-space (resp., well-filtered space) iff the function space $\mathbb{C}(X, Y)$ equipped with the Isbell topology is a d-space (resp., well-filtered space). It is shown that for any $T_0$ space X for which the Scott space $\Sigma \mathcal O(X)$ is non-sober, the function space $\mathbb{C}(X, \Sigma 2)$ equipped with the Isbell topology is not sober. The function spaces $\mathbb{C}(X, Y)$ equipped with the Scott topology, the compact-open topology and the pointwise convergence topology are also discussed. Our study also leads to a number of questions, whose answers will deepen our understanding of the function spaces related to H-sober spaces.

It has been suggested that added sugar intake is associated with non-alcoholic fatty liver disease (NAFLD). However, previous studies only focused on sugar-sweetened beverages; the evidence for associations with total added sugars and their sources is scarce. This study aimed to examine the associations of total added sugars, their physical forms (liquid v. solid) and food sources with risk of NAFLD among adults in Tianjin, China. We used data from 15 538 participants, free of NAFLD, other liver diseases, CVD, cancer or diabetes at baseline (2013–2018 years). Added sugar intake was estimated from a validated 100-item FFQ. NAFLD was diagnosed by ultrasonography after exclusion of other causes of liver diseases. Multivariable Cox proportional hazards models were fitted to calculate hazard ratios (HR) and corresponding 95 % CI for NAFLD risk with added sugar intake. During a median follow-up of 4·2 years, 3476 incident NAFLD cases were documented. After adjusting for age, sex, BMI and its change from baseline to follow-up, lifestyle factors, personal and family medical history and overall diet quality, the multivariable HR of NAFLD risk were 1·18 (95 % CI 1·06, 1·32) for total added sugars, 1·20 (95 % CI 1·08, 1·33) for liquid added sugars and 0·96 (95 % CI 0·86, 1·07) for solid added sugars when comparing the highest quartiles of intake with the lowest quartiles of intake. In this prospective cohort of Chinese adults, higher intakes of total added sugars and liquid added sugars, but not solid added sugars, were associated with a higher risk of NAFLD.

The association between dietary Cu intake and mortality risk remains uncertain. We aimed to investigate the relationship of dietary Cu intake with all-cause mortality among Chinese adults. A total of 17 310 participants from the China Health and Nutrition Survey, a national ongoing open cohort of Chinese participants, were included in the analysis. Dietary intake was measured by three consecutive 24-h dietary recalls in combination with a weighing inventory over the same 3 d. The average intakes of the 3-d dietary macronutrients and micronutrients were calculated. The study outcome was all-cause mortality. During a median follow-up of 9·0 years, 1324 (7·6 %) participants died. After adjusting for sex, age, BMI, ever alcohol drinking, ever smoking, education levels, occupations, urban or rural residents, systolic blood pressure, diastolic blood pressure and the intakes of fat, protein and carbohydrate, the association between dietary Cu intake and all-cause mortality followed a J-shape (P for nonlinearity = 0·047). When dietary Cu intake was assessed as quartiles, compared with those in the first quartile (<1·60 mg/d), the adjusted hazard ratios for all-cause mortality were 0·87 (95 % CI (0·71, 1·07)), 0·98 (95 % CI (0·79, 1·21)) and 1·49 (95 % CI (1·19, 1·86)), respectively, in participants in the second (1·60–<1·83 mg/d), third (1·83–<2·09 mg/d) and fourth (≥2·09 mg/d) quartiles. A series of subgroup analyses and sensitivity analyses showed similar results. Overall, our findings emphasised the importance of maintaining optimal dietary Cu intake levels for prevention of premature death.

Understanding predator–prey interactions is essential for successful pest management by using predators, especially for the suppression of novel invasive pest. The green lacewing Chrysopa formosa is a promising polyphagous predator that is widely used in the biocontrol of various pests in China, but information on the control efficiency of this predator against the seriously invasive pest Spodoptera frugiperda and native Spodoptera litura is limited. Here we evaluated the predation efficiency of C. formosa adults on eggs and first- to third-instar larvae of S. frugiperda and S. litura through functional response experiments and determined the consumption capacity and prey preference of this chrysopid. Adults of C. formosa had a high consumption of eggs and earlier instar larvae of both prey species, and displayed a type II functional response on all prey stages. Attack rates of the chrysopid on different prey stages were statistically similar, but the handling time increased notably as the prey developed. The highest predation efficiency and shortest-handling time were observed for C. formosa feeding on Spodoptera eggs, followed by the first-instar larvae. C. formosa exhibited a significant preference for S. litura over S. frugiperda in a two-prey system. In addition, we summarized the functional response and predation efficiency of several chrysopids against noctuid pests and made a comparison with the results obtained from C. formosa. These results indicate that C. formosa has potential as an agent for biological control of noctuid pests, particularly for the newly invasive pest S. frugiperda in China.