The association between time-restricted eating (TRE) and the risk of non-alcoholic fatty liver disease (NAFLD) is less studied. Moreover, whether the association is independent of physical exercise or diet quality or quantity is uncertain. In this nationwide cross-sectional study of 3813 participants, the timing of food intakes was recorded by 24-h recalls; NAFLD was defined through vibration-controlled transient elastography in the absence of other causes of chronic liver disease. OR and 95 % CI were estimated using logistic regression. Participants with daily eating window of ≤ 8 h had lower odds of NAFLD (OR = 0·70, 95 % CI: 0·52, 0·93), compared with those with ≥ 10 h window. Early (05.00–15.00) and late TRE (11.00–21.00) showed inverse associations with NAFLD prevalence without statistical heterogeneity (P heterogeneity = 0·649) with OR of 0·73 (95 % CI: 0·36, 1·47) and 0·61 (95 % CI: 0·44, 0·84), respectively. Such inverse association seemed stronger in participants with lower energy intake (OR = 0·58, 95 % CI: 0·38, 0·89, P interaction = 0·020). There are no statistical differences in the TRE-NAFLD associations according to physical activity (P interaction = 0·390) or diet quality (P interaction = 0·110). TRE might be associated with lower likelihood of NAFLD. Such inverse association is independent of physical activity and diet quality and appears stronger in individuals consuming lower energy. Given the potential misclassification of TRE based on one- or two-day recall in the analysis, epidemiological studies with validated methods for measuring the habitual timing of dietary intake are warranted.

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 paper presents a study on the design and modeling of a novel pneumatic self-repairing soft actuator. The self-repairing soft actuator is composed of driving element, heating element, and repairing element. The driving element completes the deformation of the self-repairing soft actuator. The heating element and the repairing element complete the self-repairing function of the self-repairing soft actuator. A model used to optimize the structure is established, and the structure of the self-repairing soft actuator is determined through finite element analysis and experiment. The self-repairing time model of the soft actuator is established. The influences of different factors on the self-repairing effect and the self-repairing time are analyzed. The self-repairing scheme of the soft actuator is determined. Experiments show that the shortest time for the self-repairing soft actuator to complete the self-repairing process is 83 min. When the self-repairing soft actuator works normally, the bending angle can reach 129.8° and the bending force can reach 24.96 N. After repairing, the bending angle can reach 108.2°, and the bending force can reach 21.85 N. The repaired soft actuator can complete normal locomotion.

In this paper, we study the ergodicity of the geodesic flows on surfaces with no focal points. Let M be a smooth connected and closed surface equipped with a $C^{\infty }$ Riemannian metric g, whose genus $\mathfrak {g} \geq 2$ . Suppose that $(M,g)$ has no focal points. We prove that the geodesic flow on the unit tangent bundle of M is ergodic with respect to the Liouville measure, under the assumption that the set of points on M with negative curvature has at most finitely many connected components.

Recently, Scholten and Read (2014) found new violations of dominance in intertemporal choice. Although adding a small receipt before a delayed payment or adding a small delayed receipt after an immediate receipt makes the prospect objectively better, it decreases the preference for that prospect (better is worse). Conversely, although adding a small payment before a delayed receipt or adding a small delayed payment after an immediate payment makes the prospect objectively worse, it increases the preference for that prospect (worse is better). Scholten and Read explained these violations in terms of a preference for improvement. However, to produce violations such as these, we find that the temporal sequences need not be constructed as Scholten and Read suggested. In this study, adding a small receipt before a dated receipt (thus constructed as improving) or adding a receipt after a dated payment (thus constructed as improving) decreases preferences for those prospects. Conversely, adding a small payment after a dated receipt (thus constructed as deteriorating) or adding a small payment before a delayed payment (thus constructed as deteriorating) increases preferences for those prospects.

In this work, theoretical modelling, quasi-three-dimensional (quasi-3D) simulations and micromodel experiments are conducted to study spontaneous imbibition with gravity in porous media micromodels. By establishing the force balance governing the spontaneous imbibition process, we develop a theoretical model for predicting the imbibition length against time in a rectangular capillary. The theoretical model is then extended to the prediction of a compact displacement process in a micromodel by using an equivalent width, which is derived by analogising the micromodel to a rectangular capillary. By simulating spontaneous imbibition in a rectangular capillary with various aspect ratios ($\varepsilon$), we show that the application condition of the quasi-3D method is $\varepsilon \leqslant 1/3$. Next, we simulate spontaneous imbibition in micromodels with various geometries and flow conditions. Fingering and compact displacement are identified for varying viscosity ratios and gravitational accelerations. At low (high) viscosity ratio of wetting to non-wetting fluids, an upward (downward) gravity can promote the stability of the wetting front, favouring the transition from fingering to compact displacement. In addition, we find that the depth-oriented interface curvature dominates the capillary effect during the imbibition, and such a mechanism is considered by introducing an equivalent contact angle into the theoretical model. With the help of equivalent width and contact angle, the theoretical model is shown to provide satisfactory prediction of the compact displacement process. Finally, a micromodel experiment is presented to further verify the developed theoretical model and the quasi-3D simulation.

The Tian Shan mountain range, known as the water towers of Central Asia, plays a key role in local water supply, yet large uncertainties remain about the amount of water that is stored in its glaciers. In this study, we assess the impact of the boundary conditions on ice thickness estimates using two inversion models: a mass conservation (MC) model and a basal shear stress (BS) model. We compare the widely used Randolph Glacier Inventory version 6 with the updated Glacier Area Mapping for Discharge from the Asian Mountains glacier inventory, as well as two digital elevation models (SRTM DEM and Copernicus DEM). The results show that the ice volume (in ~2000 CE) in the Tian Shan range is 661.0 ± 163.5 km3 for the MC model and 552.8 ± 85.3 km3 for the BS model. There are strong regional differences due to inventory, especially for glaciers in China (17–25%). However, the effect of different DEM sources on ice volume estimation is limited. By the end of the 21st century, the projected mass loss differences between inventories are higher than between adjacent emission scenarios, illustrating the vital importance of high-quality inventories. These differences should be carefully considered during water resource planning.

In the paper, a novel modular soft robot that can crawl and turn is presented. The modular soft robot is composed of multiple drive modules connected in series, including one head module, one tail module and three body modules. Each module is actuated by the air chamber. Due to the nonlinear performance of the air chamber, the strain energy function of the air chamber is established. The relationship between the displacement of the air chamber expansion wall and the inflation pressure is obtained, and the manufacturing parameters of the air chamber are determined. By dividing the body of the robot into a series of continuous flexible models, the driving force and the friction force of the robot in locomotion are analyzed. An inflation and deflation control method is presented to complete the locomotion. According to the experiment, the crawling speed of the robot can reach 15.53 mm/s (0.03 body length per second). The turning speed of the robot can reach 1.273 °/s. The robot can crawl and turn on the rough blanket surface effectively. The robot can explore and move in a complex and changeable environment.

The aim of this work is to design and model a novel modular bionic soft robot for crawling and crossing obstacles. The modular bionic soft robot is composed of several serial driving soft modules, each module is composed of two parallel soft actuators. By analyzing the influence of working pressure and manufacturing size on the stiffness of the modular bionic soft robot, the nonlinear variable stiffness model of the modular bionic soft robot is established. Based on this model, the spatial states and design parameters of the modular bionic soft robot are discussed when the modular bionic soft robot can pass through the obstacle. Experiments show that when the inflation air pressure of the modular bionic soft robot is 70 kPa, its speed can reach 7.89 mm/s and the height of obstacles passed by it can reach 42.8 mm. The feasibility of the proposed modular bionic soft robot and nonlinear variable stiffness model is verified by locomotion experiments.

The Middle Miocene Climatic Optimum is known for abrupt events during the global cooling trend of the past 20 Ma. Its identification in the Tibetan Plateau can help explain the cause of the critical Middle Miocene climate transition in Central Asia. In this study, fine-grained mixed sediments widely distributed in the Miocene Qaidam Lake in the northern Tibetan Plateau were used as a sensitive indicator for palaeoclimate. Their geochemical characteristics were investigated, together with an analysis of 2600 m long successive gamma-ray logging data from the whole JS2 drillcore, to understand the mid-Miocene climate transition in the Tibetan Plateau. By comparing the gamma-ray curve of the mixed sediments with global temperature, the Middle Miocene Climatic Optimum event can be easily identified. Further, the detailed petrological features and geochemical data of lacustrine fine-grained mixed sediments from a 400 m drillcore show oxidizing, high-sedimentation rate and brackish-saline water conditions in a semi-arid climate during the Middle Miocene period, demonstrating a dryer climate in the Qaidam Basin than in the monsoon-sensitive regions in Central Asia. These fine-grained mixed sediments have recorded climate drying before 15.3 Ma that represents a climatic transition within the Middle Miocene Climatic Optimum; increasing carbonate-rich mixed sediments, decreasing algal limestone layers and decreasing lacustrine organic matter are indicators of this transition. Regional tectonic events include the retreat of the Paratethys from Central Asia at ∼15 Ma and the synchronous tectonic reorganization of the Altyn-Tagh fault system and the northeastern Tibetan Plateau. We find that global climate change is the primary factor affecting the overall characteristics and changes of the Neogene climate in the Qaidam Basin, including the occurrence of the Middle Miocene Climatic Optimum and the cooling and drying tendency, while the regional events are a secondary factor.

This paper proposes a novel method of error mitigation for stereo visual odometry (VO) applied in land vehicles. A non-holonomic constraint (NHC), which imposes physical constraint to the rightward velocity of a land vehicle, is implemented as an observation in an extended Kalman filter (EKF) to reduce the drift of stereo VO. The EKF state vector includes position errors in an Earth-centred, Earth-fixed (ECEF) frame, velocity errors in the camera frame, angular rate errors and attitude errors. All the related equations are described and presented in detail. In this approach, no additional sensors are used but NHC, namely velocity constraint in the right direction , is applied as an external measurement to improve the accuracy. Tests are conducted with the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) datasets. Results show that the relative horizontal positioning error improved from 0⋅63% to 0⋅22% on average with the application of the velocity constraints. The maximum and root mean square of the horizontal error with velocity constraints are both reduced to less than half of the error with stand-alone stereo VO.

A12-week experiment was conducted to evaluate the influences of thiamine ongrowth performance, and intestinal mitochondrial biogenesis and function of Megalobramaamblycephala fed a high-carbohydrate (HC) diet. Fish (24·73 (sem 0·45) g) were randomly assigned to one of four diets: two carbohydrate (CHO) levels (30 and 45 %) and two thiamine levels (0 and 1·5 mg/kg). HC diets significantly decreased DGC, GRMBW, FIMBW, intestinal activities of amylase, lipase, Na+, K+-ATPase, CK, complexes I, III and IV, intestinal ML, number of mitochondrial per field, ΔΨm, the P-AMPK: T-AMPK ratio, PGC-1β protein expression as well as the transcriptions of AMPKα1, AMPKα2, PGC-1β, mitochondrial transcription factor A, Opa-1, ND-1 and COX-1 and 2, while the opposite was true for ATP, AMP and reactive oxygen species, and the transcriptions of dynamin-related protein-1, fission-1 and mitochondrial fission factor. Dietarythiamine concentrations significantly increased DGC, GRMBW, intestinal activities of amylase, Na+, K+-ATPase, CK, complexes I and IV, intestinal ML, number of mitochondrial per field, ΔΨm, the P-AMPK:T-AMPK ratio, PGC-1β protein expression as well as the transcriptions of AMPKα1, AMPKα2, PGC-1β, Opa-1, ND-1, COX-1 and 2, SGLT-1 and GLUT-2. Furthermore, a significant interaction between dietary CHO and thiamine was observed in DGC, GRMBW, intestinal activities of amylase, CK, complexes I and IV, ΔΨm, the AMP:ATP ratio, the P-AMPK:T-AMPK ratio, PGC-1β protein expression as well as the transcriptions of AMPKα1, AMPKα2, PGC-1β, Opa-1, COX-1 and 2, SGLT-1 and GLUT-2. Overall, thiamine supplementation improved growth performance, and intestinal mitochondrial biogenesis and function of M. amblycephala fed HC diets.

This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

Shifts in the maternal gut microbiota have been implicated in the development of gestational diabetes mellitus (GDM). Understanding the interaction between gut microbiota and host glucose metabolism will provide a new target of prediction and treatment. In this nested case-control study, we aimed to investigate the causal effects of gut microbiota from GDM patients on the glucose metabolism of germ-free (GF) mice. Stool and peripheral blood samples, as well as clinical information, were collected from 45 GDM patients and 45 healthy controls (matched by age and prepregnancy body mass index (BMI)) in the first and second trimester. Gut microbiota profiles were explored by next-generation sequencing of the 16S rRNA gene, and inflammatory factors in peripheral blood were analyzed by enzyme-linked immunosorbent assay. Fecal samples from GDM and non-GDM donors were transferred to GF mice. The gut microbiota of women with GDM showed reduced richness, specifically decreased Bacteroides and Akkermansia, as well as increased Faecalibacterium. The relative abundance of Akkermansia was negatively associated with blood glucose levels, and the relative abundance of Faecalibacterium was positively related to inflammatory factor concentrations. The transfer of fecal microbiota from GDM and non-GDM donors to GF mice resulted in different gut microbiota colonization patterns, and hyperglycemia was induced in mice that received GDM donor microbiota. These results suggested that the shifting pattern of gut microbiota in GDM patients contributed to disease pathogenesis.

This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.