Gully regulates snowmelt runoff, sediment and nutrient loss processes in Mollisols region of Northeast China.

Gully is a prominent indicator of land degradation in agroecosystems, functioning as a crucial pathway connecting upslopes to downstream channels. However, little is known about how gully regulates runoff, sediment, and nutrient loss processes in the catchment during snowmelt. In this study, we monitored these processes in situ at both the gully head (the upslope accumulated catchment of the gully head, CGH) and outlet of two representative and typical gully-dominated catchments (F1 and F2) during snowmelt in Mollisols region of Northeast China. Our results showed that runoff discharge of CGH and outlet exhibited a multi-peak trend during snowmelt, driven by the transition from snow melting to soil thawing. This transition resulted in distinct runoff patterns in both CGH and outlet, with significant differences in their response to air temperature. The total runoff yield of CGH accounted for 57.8 % in F1 and 40.6 % in F2 of the total runoff yield of the outlet. Notably, the peak sediment concentration displayed a marked lag compared to the peak runoff discharge, primarily dominated by the increased sensitivity of gully erosion after the thawing of gully slopes. Gully erosion was the main source of sediment yield in the catchment, contributing 98.2 % in F1 and 96.6 % in F2. Furthermore, nutrient concentrations exhibited a decreasing trend during snowmelt. The comparison of high nutrient concentrations in CGH and relatively low nutrient concentrations in outlet highlighted the gully's role in intercepting and diluting runoff nutrients. Hysteresis analysis confirmed the differential contribution of CGH and gully to nutrient sources. CGH accounting for 50.9 % and 93.3 % of runoff TN and runoff TP loss, while contributing only 8.3 % and 5.8 % to sediment TN and sediment TP loss, respectively. These findings offer valuable insights for effective erosion control and nonpoint source pollution management in gully-dominated agroecosystems during snowmelt.

Evolutionary Multiobjective Molecule Optimization in an Implicit Chemical Space.

Optimization techniques play a pivotal role in advancing drug development, serving as the foundation of numerous generative methods tailored to efficiently design optimized molecules derived from existing lead compounds. However, existing methods often encounter difficulties in generating diverse, novel, and high-property molecules that simultaneously optimize multiple drug properties. To overcome this bottleneck, we propose a multiobjective molecule optimization framework (MOMO). MOMO employs a specially designed Pareto-based multiproperty evaluation strategy at the molecular sequence level to guide the evolutionary search in an implicit chemical space. A comparative analysis of MOMO with five state-of-the-art methods across two benchmark multiproperty molecule optimization tasks reveals that MOMO markedly outperforms them in terms of diversity, novelty, and optimized properties. The practical applicability of MOMO in drug discovery has also been validated on four challenging tasks in the real-world discovery problem. These results suggest that MOMO can provide a useful tool to facilitate molecule optimization problems with multiple properties.

Healthy lifestyles and rapid progression of carotid plaque in population with atherosclerosis: A prospective cohort study in China.

Background: Healthy lifestyles are effective means to reduce major cardiovascular events. However, little is known about the association of healthy lifestyles with development of carotid atherosclerosis at the early stage of cardiovascular diseases (CVDs). Methods: We enrolled participants from Fujian province in the China PEACE MPP project. We calculated a healthy lifestyle score by adherence to non-smoking, sufficient physical activity, healthy diet and healthy body mass index. Cox proportional hazards regression models and restricted cubic splines (RCS) were used to explore the association between the healthy lifestyles and rapid progression of carotid plaque. Results: 8379 participants were included (mean age: 60.6 ± 8.3 years, 54.6 % female), with a median follow-up of 1.2 years (inter quartile range: 1.0-1.6). RCS showed a significant inverse association between the healthy lifestyle score and progression of carotid plaque. Participants with "intermediate" (HR: 0.72 [95 % confidence interval (CI): 0.65-0.80]) or "ideal" (HR: 0.68 [0.59-0.78]) adherence to healthy lifestyles had a lower risk of progression of carotid plaque compared to those with "poor" adherence. Age, sex, occupation, income, residence type and metabolic status were significant factors influencing the relationship. Farmers benefited more in non-smoking and sufficient physical activity compared to non-farmers, and participants with lower income or without dyslipidaemia benefited more in sufficient physical activity and healthy diet compared to their counterparts (p-for-interaction < 0.05). Conclusions: Healthy lifestyles were associated with lower risk of progression of carotid plaque in populations with atherosclerosis. Promotion of healthy lifestyles from the early stage of carotid atherosclerosis could reduce the burden of CVDs in China.

Healthy lifestyle, statin, and mortality in people with high CVD risk: A nationwide population-based cohort study.

Objective: To examine the joint association of healthy lifestyles and statin use with all-cause and cardiovascular mortality in high-risk individuals, and evaluate the survival benefits by life expectancy. Methods: During 2015-2021, participants aged 35-75 years were recruited by the China Health Evaluation And risk Reduction through nationwide Teamwork. Based on number of healthy lifestyles related to smoking, alcohol drinking, physical activity, and diet, we categorized them into: very healthy (3-4), healthy (2), and unhealthy (0-1). Statin use was determined by self-report taking statin in last two weeks. Results: Among the 265,209 included participants at high risk, 6979 deaths were observed, including 3236 CVD deaths during a median 3.6 years of follow-up. Individuals taking statin and with a very healthy lifestyle had the lowest risk of all-cause (HR: 0.70; 95 %CI: 0.57-0.87) and cardiovascular mortality (0.56; 0.40-0.79), compared with statin non-users with an unhealthy lifestyle. High-risk participants taking statin and with a very healthy lifestyle had the highest years of life gained (5.90 years at 35-year-old [4.14-7.67; P < 0.001]) compared with statin non-users with an unhealthy lifestyle among high-risk people. And their life expectancy was comparable with those without high risk but with a very healthy lifestyle (4.49 vs. 4.68 years). Conclusion: The combination of preventive medication and multiple healthy lifestyles was associated with lower risk of all-cause and cardiovascular mortality and largest survival benefits. Integrated strategy to improve long-term health for high-risk people was urgently needed.

Active Property-Structure Integrated Reconfiguration of Individual Resonant Nanoparticles.

Property-structure reconfigurable nanoparticles (NPs) provide additional flexibility for effectively and flexibly manipulating light at the nanoscale. This has facilitated the development of various multifunctional and high-performance nanophotonic devices. Resonant NPs based on dielectric active materials, especially phase change materials, are particularly promising for achieving reconfigurability. However, the on-demand control of the properties, especially the morphology, in individual dielectric resonant NP remains a significant challenge. In this study, we present an all-optical approach for one-step fabrication of Ge2Sb2Te5 (GST) hemispherical NPs, integrated active reversible phase-state switching, and morphology reshaping. Reversible optical switching is demonstrated, attributed to reversible phase-state changes, along with unidirectional modifications to their scattering intensity resulting from morphology reshaping. This novel technology allows the precise adjustment of each structural pixel without affecting the overall functionality of the switchable nanophotonic device. It is highly suitable for applications in single-pixel-addressable active optical devices, structural color displays, and information storage, among others.

Biosynthesis of eriodictyol in citrus waster by endowing P450BM3 activity of naringenin hydroxylation.

The flavonoid naringenin is abundantly present in pomelo peels, and the unprocessed naringenin in wastes is not friendly for the environment once discarded directly. Fortunately, the hydroxylated product of eriodictyol from naringenin exhibits remarkable antioxidant and anticancer properties. The P450s was suggested promising for the bioconversion of the flavonoids, but less naturally existed P450s show hydroxylation activity to C3' of the naringenin. By well analyzing the catalytic mechanism and the conformations of the naringenin in P450, we proposed that the intermediate Cmpd I ((porphyrin)Fe = O) is more reasonable as key conformation for the hydrolyzation, and the distance between C3'/C5' of naringenin to the O atom of CmpdI determines the hydroxylating activity for the naringenin. Thus, the "flying kite model" that gradually drags the C-H bond of the substrate to the O atom of CmpdI was put forward for rational design. With ab initio design, we successfully endowed the self-sufficient P450-BM3 hydroxylic activity to naringenin and obtained mutant M5-5, with kcat, Km, and kcat/Km values of 230.45 min-1, 310.48 µM, and 0.742 min-1 µM-1, respectively. Furthermore, the mutant M4186 was screened with kcat/Km of 4.28-fold highly improved than the reported M13. The M4186 also exhibited 62.57% yield of eriodictyol, more suitable for the industrial application. This study provided a theoretical guide for the rational design of P450s to the nonnative compounds. KEY POINTS: •The compound I is proposed as the starting point for the rational design of the P450BM3 •"Flying kite model" is proposed based on the distance between O of Cmpd I and C3'/C5' of naringenin •Mutant M15-5 with 1.6-fold of activity than M13 was obtained by ab initio modification.

Multigranularity Surrogate Modeling for Evolutionary Multiobjective Optimization With Expensive Constraints.

Multiobjective optimization problems (MOPs) with expensive constraints pose stiff challenges to existing surrogate-assisted evolutionary algorithms (SAEAs) in a very limited computational cost, due to the fact that the number of expensive constraints for an MOP is often large. For existing SAEAs, they always approximate constraint functions in a single granularity, namely, approximating the constraint violation (CV, coarse-grained) or each constraint (fine-grained). However, the landscape of CV is often too complex to be accurately approximated by a surrogate model. Although the modeling of each constraint function may be simpler than that of CV, approximating all the constraint functions independently may result in tremendous cumulative errors and high computational costs. To address this issue, in this article, we develop a multigranularity surrogate modeling framework for evolutionary algorithms (EAs), where the approximation granularity of constraint surrogates is adaptively determined by the position of the population in the fitness landscape. Moreover, a dedicated model management strategy is also developed to reduce the impact resulting from the errors introduced by constraint surrogates and prevent the population from trapping into local optima. To evaluate the performance of the proposed framework, an implementation called K-MGSAEA is proposed, and the experimental results on a large number of test problems show that the proposed framework is superior to seven state-of-the-art competitors.

Large-Scale Data-Driven Optimization in Deep Modeling With an Intelligent Decision-Making Mechanism.

This study focuses on building an intelligent decision-making attention mechanism in which the channel relationship and conduct feature maps among specific deep Dense ConvNet blocks are connected to each other. Thus, develop a novel freezing network with a pyramid spatial channel attention mechanism (FPSC-Net) in deep modeling. This model studies how specific design choices in the large-scale data-driven optimization and creation process affect the balance between the accuracy and effectiveness of the designed deep intelligent model. To this end, this study presents a novel architecture unit, which is termed as the "Activate-and-Freeze" block on popular and highly competitive datasets. In order to extract informative features by fusing spatial and channel-wise information together within local receptive fields and boost the representation power, this study constructs a Dense-attention module (pyramid spatial channel (PSC) attention) to perform feature recalibration, and through the PSC attention to model the interdependence among convolution feature channels. We join the PSC attention module in the activating and back-freezing strategy to search for one of the most important parts of the network for extraction and optimization. Experiments on various large-scale datasets demonstrate that the proposed method can achieve substantially better performance for improving the ConvNets representation power than the other state-of-the-art deep models.

The effects of temporal cues, point-light displays, and faces on speech identification and listening effort.

Among the most robust findings in speech research is that the presence of a talking face improves the intelligibility of spoken language. Talking faces supplement the auditory signal by providing fine phonetic cues based on the placement of the articulators, as well as temporal cues to when speech is occurring. In this study, we varied the amount of information contained in the visual signal, ranging from temporal information alone to a natural talking face. Participants were presented with spoken sentences in energetic or informational masking in four different visual conditions: audio-only, a modulating circle providing temporal cues to salient features of the speech, a digitally rendered point-light display showing lip movement, and a natural talking face. We assessed both sentence identification accuracy and self-reported listening effort. Audiovisual benefit for intelligibility was observed for the natural face in both informational and energetic masking, but the digitally rendered point-light display only provided benefit in energetic masking. Intelligibility for speech accompanied by the modulating circle did not differ from the audio-only conditions in either masker type. Thus, the temporal cues used here were insufficient to improve speech intelligibility in noise, but some types of digital point-light displays may contain enough phonetic detail to produce modest improvements in speech identification in noise.

Influence of Socioeconomic Gender Inequality on Sex Disparities in Prevention and Outcome of Cardiovascular Disease: Data From a Nationwide Population Cohort in China.

Background Knowledge gaps remain in how gender-related socioeconomic inequality affects sex disparities in cardiovascular diseases (CVD) prevention and outcome. Methods and Results Based on a nationwide population cohort, we enrolled 3 737 036 residents aged 35 to 75 years (2014-2021). Age-standardized sex differences and the effect of gender-related socioeconomic inequality (Gender Inequality Index) on sex disparities were explored in 9 CVD prevention indicators. Compared with men, women had seemingly better primary prevention (aspirin usage: relative risk [RR], 1.24 [95% CI, 1.18-1.31] and statin usage: RR, 1.48 [95% CI, 1.39-1.57]); however, women's status became insignificant or even worse when adjusted for metabolic factors. In secondary prevention, the sex disparities in usage of aspirin (RR, 0.65 [95% CI, 0.63-0.68]) and statin (RR, 0.63 [95% CI, 0.61-0.66]) were explicitly larger than disparities in usage of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (RR, 0.88 [95% CI, 0.84-0.91]) or ß blockers (RR, 0.67 [95% CI, 0.63-0.71]). Nevertheless, women had better hypertension awareness (RR, 1.09 [95% CI, 1.09-1.10]), similar hypertension control (RR, 1.01 [95% CI, 1.00-1.02]), and lower CVD mortality (hazard ratio, 0.46 [95% CI, 0.45-0.47]). Heterogeneities of sex disparities existed across all subgroups. Significant correlations existed between regional Gender Inequality Index values and sex disparities in usage of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (Spearman correlation coefficient, r=-0.57, P=0.0013), hypertension control (r=-0.62, P=0.0007), and CVD mortality (r=0.45, P=0.014), which remained significant after adjusting for economic factors. Conclusions Notable sex disparities remain in CVD prevention and outcomes, with large subgroup heterogeneities. Gendered socioeconomic factors could reinforce such disparities. A sex-specific perspective factoring in socioeconomic disadvantages could facilitate more targeted prevention policy making.

Efficient Surrogate Modeling Method for Evolutionary Algorithm to Solve Bilevel Optimization Problems.

The purpose of this study was to develop an evolutionary algorithm (EA) with bilevel surrogate modeling, called BL-SAEA, for tackling bilevel optimization problems (BLOPs), in which an upper level problem is to be solved subject to the optimality of a corresponding lower level problem. The motivation of this article is that the extensive lower level optimization required by each upper level solution consumes too many function evaluations, leading to poor optimization performance of EAs. To this end, during the upper level optimization, the BL-SAEA builds an upper level surrogate model to select several promising upper level solutions for the lower level optimization. Because only a small number of upper level solutions require the lower level optimization, the number of function evaluations can be considerably reduced. During the lower level optimization, the BL-SAEA constructs multiple lower level surrogate models to initialize the population of the lower level optimization, thus further decreasing the number of function evaluations. Experimental results on two widely used benchmarks and two real-world BLOPs demonstrate the superiority of our proposed algorithm over six state-of-the-art algorithms in terms of effectiveness and efficiency.

Protein intrinsically disordered region prediction by combining neural architecture search and multi-objective genetic algorithm.

BACKGROUND: Intrinsically disordered regions (IDRs) are widely distributed in proteins and related to many important biological functions. Accurately identifying IDRs is of great significance for protein structure and function analysis. Because the long disordered regions (LDRs) and short disordered regions (SDRs) share different characteristics, the existing predictors fail to achieve better and more stable performance on datasets with different ratios between LDRs and SDRs. There are two main reasons. First, the existing predictors construct network structures based on their own experiences such as convolutional neural network (CNN) which is used to extract the feature of neighboring residues in protein, and long short-term memory (LSTM) is used to extract the long-distance dependencies feature of protein residues. But these networks cannot capture the hidden feature associated with the length-dependent between residues. Second, many algorithms based on deep learning have been proposed but the complementarity of the existing predictors is not fully explored and used. RESULTS: In this study, the neural architecture search (NAS) algorithm was employed to automatically construct the network structures so as to capture the hidden features in protein sequences. In order to stably predict both the LDRs and SDRs, the model constructed by NAS was combined with length-dependent models for capturing the unique features of SDRs or LDRs and general models for capturing the common features between LDRs and SDRs. A new predictor called IDP-Fusion was proposed. CONCLUSIONS: Experimental results showed that IDP-Fusion can achieve more stable performance than the other existing predictors on independent test sets with different ratios between SDRs and LDRs.

Prediction of gaseous medium insulation strength based on electrostatic potential on real space function isosurface.

CONTEXT: SF6 is widely used in electrical equipment due to its chemical stability and insulation strength, but it is a strong greenhouse gas and its use has been restricted internationally. In order to reduce the SF6 usage, it is needed to find a replacement gas for SF6. Electrical breakdown test is always adopted to select potential substitutes, but it is resource and time intensive. Thus, a structure-activity relationship model is needed to effectively predict the gas insulation strength. In this work, we calculated the isosurface electrostatic potential of 68 gas molecules in case of electron probability density, Laplacian of electron density, electron localization function, and localized orbital function. The distribution characteristics of these four real space functions were analyzed. Furthermore, correlation between the electrostatic potential parameters and insulation strength was presented. Finally, a prediction model for insulation strength of gaseous medium was established. Using the electrostatic potential parameter on the localized orbital locator function with a threshold of 0.05 a.u., the prediction model achieved the best performance with a coefficient of determination of 0.860 and a mean squared error of 0.0663. METHODS: The quantization calculation tool used in this work is the GAUSSIAN 16 software. The M06-2X method with the 6-311G++(d, p) basis set is used to optimize the molecular structure and output stable wavefunction files. Then the wavefunction analysis software Multiwfn is used to plot the contour map of the gas molecules and calculate the radial distribution patterns.

The 'Ironclad friendship' of China-Cambodia, lays the first step in the foundation of early diagnosis and treatment of asymptomatic congenital heart Defects- A multi-national screening and intervention project, 2017-2020.

BACKGROUND: Congenital heart disease (CHD) is the leading cause of mortality in childhood worldwide. However, a large number of children with CHD are not diagnosed promptly in low- and middle-income regions, due to limited healthcare resources and lack the ability of prenatal and postnatal ultrasound examinations. The research on asymptomatic CHD in the community is still blank, resulting in a large number of children with asymptomatic CHD can not be found and treated in time. Through the China-Cambodia collaborative health care initiative, the project team conducted research, screened children's CHD through a sampling survey in China and Cambodia, collected relevant data, and retrospectively analyzed the data of all eligible patients. OBJECTIVES: The project aimed to evaluate the prevalence of asymptomatic CHD in a sample population of 3-18years old and effects on their growth status and treatment outcomes. METHODS: We examined the prevalence of 'asymptomatic CHD' among 3-18years old children and adolescents at the township/county levels in the two participating. A total of eight provinces in China and five provinces in Cambodia were analyzed from 2017 to 2020. During 1 year follow-up after treatment, the differences in heights and weights of the treated and control groups were evaluated. RESULTS: Among the 3,068,075 participants screened from 2017 to 2020, 3967 patients with asymptomatic CHD requiring treatment were identified [0.130%, 95% confidence interval (CI) 0.126 -0.134%]. The prevalence rate of CHD ranged from 0.02 to 0.88%, and was negatively related to local per capita GDP (p = 0.028). The average height of 3310 treated CHD patients were 2.23% (95% CI: -2.51%~-1.9%) lower than that of the standard group and the average weight was - 6.41% (95% CI: -7.17%~-5.65%) lower, the developmental gap widening with advancing age. One year after treatment, the relative height difference remained comparable while that, in weight was reduced by 5.68% (95% CI: 4.27% ~7.09%). CONCLUSIONS: Asymptomatic CHD now is often overlooked and is an emerging public health challenge. Early detection and treatment are essential to lower the potential burden of heart diseases in children and adolescents.

Mechanical effects: challenges for high-field superconducting magnets.

Due to its clean products and sufficient raw materials, fusion energy is expected to become one of the main solutions of the energy crisis and ensuring the sustainable development of human society, which is a long-term strategic frontier field. The promise of fusion energy is to constrain the motion of high-temperature plasma by the high magnetic field generated by superconducting magnets, and then achieve controllable thermonuclear fusion. Fusion power is proportional to the fourth power of the magnetic field strength. Thus, future commercial fusion reactors need a higher magnetic field as the basis for sustainable development [1]. In order to verify the scientific and technological feasibility of fusion energy, China, the United States, the European Union, Russia et al. have jointly participated in the construction of the International Thermonuclear Fusion Test Reactor (ITER), which is expected to produce the first plasma discharge by 2025 [2]. Currently, China is leading the world in many fields of fusion energy research. For example, the experimental advanced superconducting Tokamak (EAST) whole-superconducting Tokamak located at the Institute of Plasma Physics in the Chinese Academy of Sciences has achieved a repeatable world record of stable plasma operation at 120 million degrees Celsius for 101 seconds, which provides a solid foundation for ITER and also China's future Independent Building Fusion Reactor (https://www.cas.cn/syky/202105/t20210528_4790357.shtml). Prof. Jiangang Li, an academician of the Chinese Academy of Engineering, participated in and completed the design and construction of EAST plasma facing componments (PFCs) engineering by the support of the national '9th five-year plan' major scientific and technological infrastructure, and presided over the completion of the national '11th five-year plan' major scientific and technological infrastructure-EAST auxiliary heating system project. He also hosted the national '13th five-year plan' major scientific and technological infrastructure-Integrated Research Facility for Critical Systems of fusion reactor comprehensive research facility for fusion technology (CRAFT). Many important scientific and technological problems have been solved and overcome by Prof. Li and his co-workers, which puts China's plasma physics research and fusion engineering technology at the forefront of global engineering.

Lightweight, highly tough and durable YBa2Cu3O7 -x superconductor.

The inherent brittleness and low sustainability of YBa2Cu3O7 -x (YBCO) bulk superconductor seriously impede its wide applications. It is a great challenge to achieve toughening of this material and maintain its invariable superconductivity at the same time. Here, we fabricate bulk YBCO composite superconductor with a density of 2.15 g cm-3, which consists of interlocking dual network construction and shows high toughness and durability. The results show that its unit normalized fracture energy at 77 K reaches 638.6 kN m-2, which is ∼14.8 times that of YBCO bulk prepared by the top-seeded melt textured growth (TSMTG) method. Its critical current shows no degradation during the toughening process. Moreover, after 10 000 cycles, the sample does not fracture with the decay of critical current at 4 K of 14.6% whereas the TSMTG sample fractures only after 25 cycles.

Cardiovascular disease prevention and mortality across 1 million urban populations in China: data from a nationwide population-based study.

BACKGROUND: China has been undergoing a rapid urbanisation. There are substantial disparities between old and new urban citizens in access to health care. We aimed to compare cardiovascular disease prevention and death risks among four distinct urban groups. METHODS: Urban residents aged 35-75 years living in 96 prefecture-level cities from 31 provinces in mainland China were enrolled in the national population-based cohort China Patient-centered Evaluative Assessment of Cardiac Events Million Persons Project. They were categorised into four groups by their former and current places of residence as follows: old-urban in situ residents (local residents in established urban areas since birth), new-urban in situ residents (local residents in newly urbanised areas established during urbanisation), urban-to-urban migrants (migrants from other urban areas), and rural-to-urban migrants (migrants from rural areas). We excluded participants with missing data for former and current places of residence, medical history, socioeconomic status, or lifestyle information. After adjusting for demographic and socioeconomic characteristics, relative risks (RRs) of cardiovascular disease prevention indicators and hazard ratios (HRs) of cardiovascular mortality and all-cause mortality of the other three population groups were estimated by modified log-Poisson models with robust standard error and Cox proportional hazard models, with old-urban in situ residents as the reference group. FINDINGS: From Sept 1, 2015, to Aug 17, 2020, 1 339 329 residents were enrolled, 270 606 were excluded for missing data in key variables, and 1 068 723 were subsequnetly included in the study. Compared with old-urban in situ residents, new-urban in situ residents were less likely to adhere to a healthy diet (RR 0·72 [95% CI 0·62-0·83]), while no significant results were observed in rural-to-urban migrants; new-urban in situ residents were less likely to use statins as primary prevention (RR 0·60 [0·46-0·79]), angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs; RR 0·78 [0·65-0·93]) and ß-blockers (RR 0·68 [0·53-0·88]) as secondary prevention; and rural-to-urban migrants were less likely to use aspirin as a primary (RR 0·67 [0·46-0·96]) and secondary (RR 0·71 [0·54-0·94]) prevention and statins (RR 0·70 [0·51-0·97]) and ACEIs or ARBs (RR 0·68 [0·50-0·93]) as secondary prevention. Furthermore, in people diagnosed with hypertension, new-urban in situ residents were less likely to have their blood pressure controlled (RR 0·79 [95% CI 0·72-0·87]), while no significant results were observed in rural-to-urban migrants. New-urban in situ residents had higher risk of cardiovascular mortality (HR 1·16 [95% CI 1·05-1·29]; p=0·005) than did old-urban in situ residents, after a median follow-up of 2·7 years (IQR 2·0-4·2). INTERPRETATION: New-urban in situ residents and rural-to-urban migrants both showed poorer utilisation of primary and secondary prevention medications than did old-urban in situ residents, while new-urban in situ residents also had lower adherence to healthy lifestyles and higher death risks. Comprehensive measures should be taken to strengthen the primary health-care system in newly urbanised areas, and promote interprovincial medical insurance reimbursement. FUNDING: Chinese Academy of Medical Sciences Innovation Fund for Medical Science and the National High Level Hospital Clinical Research Funding. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

User Identification Across Multiple Social Networks Based on Naive Bayes Model.

Recently, the problem of user identification across multiple social networks (UIAMSNs) has attracted considerable attention since it is a prerequisite for many downstream tasks and applications. Although substantial network feature-based approaches have been proposed to solve the UIAMSNs' problem, the matching degree in most of the current works is given by experience, which lacks a solid theoretical basis. To alleviate the above predicament, we propose a user identification algorithm based on naive Bayes model (UI-NBM) within the network feature-based framework. First, a matching degree index is designed based on the naive Bayes model, which can accurately measure the contributions of different common matched node pairs (MNPs) to the connection probability of unmatched node pairs (UMNPs). Second, the matching degrees of all UMNPs are formulated as the product of matrices, giving rise to the great reduction of the time complexity and the compact expression; Finally, with the idea of recursion process, more UMNPs can be iteratively predicted even when only a small amount of prior information (i.e., a few number of MNPs) is known. The experimental results on the synthetic and real cross platforms demonstrate that the method outperforms the baseline methods within the feature-based framework.

Development of a load frame for neutron diffraction and fluorescent thermometry at cryogenic temperature.

Over the years, rapidly rising interest in the mechanical properties of various materials at low temperatures has been simulated because of the growing cryogenic applications in modern engineering fields of space technology, environmental engineering, and superconductivity engineering. Realizing in situ measurement of the internal strain and the full-field strain and the temperature distribution of related materials in a cryogenic loading environment is a significant requirement for safety assessment and related research of some new large science facility projects. Here, we present a novel cryogenic load frame, which is suitable for neutron scattering measurements of internal stress at the temperature range of 6-300 K. The loading capacity is 2500 N, and the slowest loading speed is 0.001 mm/s. By replacing the vacuum chamber sealing plate with a K9 glass window, the in situ digital image correlation strain measurement can be realized. Furthermore, fluorescence thermometry has also been investigated during a heating and cooling process without deformation. Using the present design, some typical results of the 316LN stainless steel and the YBCO tape at low temperatures were introduced.