Aging plays a crucial role in the mechanisms of the impacts of genetic and environmental factors on blood pressure and serum lipids. However, to our knowledge, how the influence of genetic and environmental factors on the correlation between blood pressure and serum lipids changes with age remains to be determined. In this study, data from the Chinese National Twin Registry (CNTR) were used. Resting blood pressure, including systolic and diastolic blood pressure (SBP and DBP), and fasting serum lipids, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglycerides (TGs) were measured in 2378 participants (1189 twin pairs). Univariate and bivariate structural equation models examined the genetic and environmental influences on blood pressure and serum lipids among three age groups. All phenotypes showed moderate to high heritability (0.37–0.59) and moderate unique environmental variance (0.30–0.44). The heritability of all phenotypes showed a decreasing trend with age. Among all phenotypes, SBP and DBP showed a significant monotonic decreasing trend. For phenotype-phenotype pairs, the phenotypic correlation (Rph) of each pair ranged from −0.04 to 0.23, and the additive genetic correlation (Ra) ranged from 0.00 to 0.36. For TC&SBP, TC&DBP, TG&SBP and TGs&DBP, both the Rph and Ra declined with age, and the Ra difference between the young group and the older adult group is statistically significant (p < .05). The unique environmental correlation (Re) of each pair did not follow any pattern with age and remained relatively stable with age. In summary, we observed that the heritability of blood pressure was affected by age. Moreover, blood pressure and serum lipids shared common genetic backgrounds, and age had an impact on the phenotypic correlation and genetic correlations.

For the large redundant manipulator, due to its long working distance and large mass, the number of links (i.e., manipulator’s arms) that can be driven to move simultaneously is limited. Otherwise, the control accuracy and motion stability of the manipulator will deteriorate. Focusing on that, a weighted Newton iteration (WNI) algorithm for trajectory planning of the manipulator is firstly proposed, where the motion of the manipulator joints is controlled by a weight matrix, which is constant and related to each link’s energy consumption. To dynamically adjust the weight matrix according to kinematic constraints and acquire better energy efficiency, an adaptive WNI (AWNI) algorithm is further proposed. In AWNI, the weight matrix is adjusted in real-time during the planning process, with considerations of the kinematic constraints and the energy consumption of the manipulator. The switch of the links between the working state and the non-working state is made through the weight matrix to achieve flexible control of the manipulator motion. Two evaluation functions are established to validate the effectiveness of AWNI in energy saving and motion stability control. Taking a 6 degrees of freedom (DOF) manipulator as an example, simulation experiments on trajectory planning are carried out and the results show the effectiveness of the proposed AWNI algorithm.

It is very challenging for robots to perform grinding and polishing tasks on surfaces with unknown geometry. Most existing methods solve this problem by modeling the relationship between the force sensing information and surface normal vectors by analyzing the forces on special end tools such as spherical tools and cylindrical tools and simplified friction model. In this paper, we propose a normal vectors learning method to simultaneously control end-effector force and direction on unknown surfaces. First, the relation that mapping the force sensing information to the surface normal vectors is learned from the demonstrated data on the known plane using locally weighted regression. Next, the learned relation is used to estimate surface normal vectors on the unknown surface. To improve the force control precision on the unknown geometry surface, the adaptive force control is developed. To improve the direction control precision due to friction, the iterative learning control is developed. The proposed method is verified by comparative simulations and experiments using the Franka robot. Results show that the end-effector can be controlled perpendicular to the surface with a certain force.

Infant-directed speech (IDS) produced in laboratory settings contains acoustic cues, such as pauses, pitch changes, and vowel-lengthening that could facilitate breaking speech into smaller units, such as syntactically well-formed utterances, and the noun- and verb-phrases within them. It is unclear whether these cues are present in speech produced in more natural contexts outside the lab. We captured LENA recordings of caregiver speech to 12-month-old infants in daylong interactions (N = 49) to address this question. We found that the final positions of syntactically well-formed utterances contained greater vowel lengthening and pitch changes, and were followed by longer pauses, relative to non-final positions. However, we found no evidence that these cues were present at utterance-internal phrase boundaries. Results suggest that acoustic cues marking the boundaries of well-formed utterances are salient in everyday speech to infants and highlight the importance of characterizing IDS in a large sample of naturally-produced speech to infants.

With the aid of recently proposed word embedding algorithms, the study of semantic relatedness has progressed rapidly. However, word-level representations are still lacking for many natural language processing tasks. Various sense-level embedding learning algorithms have been proposed to address this issue. In this paper, we present a generalized model derived from existing sense retrofitting models. In this generalization, we take into account semantic relations between the senses, relation strength, and semantic strength. Experimental results show that the generalized model outperforms previous approaches on four tasks: semantic relatedness, contextual word similarity, semantic difference, and synonym selection. Based on the generalized sense retrofitting model, we also propose a standardization process on the dimensions with four settings, a neighbor expansion process from the nearest neighbors, and combinations of these two approaches. Finally, we propose a Procrustes analysis approach that inspired from bilingual mapping models for learning representations that outside of the ontology. The experimental results show the advantages of these approaches on semantic relatedness tasks.

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 (Pheterogeneity = 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, Pinteraction = 0·020). There are no statistical differences in the TRE-NAFLD associations according to physical activity (Pinteraction = 0·390) or diet quality (Pinteraction = 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.

Collision of two counterflowing gravity currents of equal densities and heights was investigated by means of three-dimensional high-resolution simulations with the goal of understanding the flow structures and energetics in the collision region in more detail. The lifetime of collision is approximately $3 \tilde {H}/\tilde {u}_f$, where $\tilde {H}$ is the depth of heavy and ambient fluids, and $\tilde {u}_f$ is the front velocity of the approaching gravity currents, and the lifetime of collision can be divided into three phases. During Phase I, $-0.2 \leqslant (\tilde {t}-\tilde {t}_c) \tilde {u}_f/\tilde {H} \leqslant 0.5$, where $\tilde {t}$ is the time, and $\tilde {t}_c$ is the time instance at which the two colliding gravity currents have fully osculated, geometric distortions of the gravity current fronts result in stretching of pre-existing vorticity in the wall-normal direction inside the fronts, and an array of vertical vortices extending throughout the updraught fluid column develop along the interface separating the two colliding gravity currents. The array of vertical vortices is responsible for the mixing between the heavy fluids of the two colliding gravity currents and for the production of turbulent kinetic energy in the collision region. The presence of the top boundary deflects the updraughts into the horizontal direction, and a number of horizontal streamwise vortices are generated close to the top boundary. During Phase II, $0.5 \leqslant (\tilde {t}-\tilde {t}_c) \tilde {u}_f/\tilde {H} \leqslant 1.2$, the horizontal streamwise vortices close to the top boundary induce turbulent buoyancy flux and break up into smaller structures. While the production of turbulent kinetic energy weakens, the rate of transfer of energy to turbulent flow due to turbulent buoyancy flux reaches its maximum and becomes the primary supply in the turbulent kinetic energy in Phase II. During Phase III, $1.2 \leqslant (\tilde {t}-\tilde {t}_c) \tilde {u}_f/\tilde {H} \leqslant 2.8$, the collided fluid slumps away from the collision region, while the production of turbulent kinetic energy, turbulent buoyancy flux and dissipation of energy attenuate. From the point of view of energetics, the production of turbulent kinetic energy and turbulent buoyancy flux transfers energy away from the mean flow to the turbulent flow during the collision. Our study complements previous experimental investigations on the collision of gravity currents in that the flow structures, spatial distribution and temporal evolution of the mean flow and turbulent flow characteristics in the collision region are presented clearly. It is our understanding that such complete information on the energy budgets in the collision region can be difficult to attain in laboratory experiments.

We aimed to investigate the association of metabolic obesity phenotypes with all-cause mortality risk in a rural Chinese population. This prospective cohort study enrolled 15 704 Chinese adults (38·86 % men) with a median age of 51·00 (interquartile range: 41·00–60·00) at baseline (2007–2008) and followed up during 2013–2014. Obesity was defined by waist circumference (WC: ≥ 90 cm for men and ≥ 80 cm for women) or waist-to-height ratio (WHtR: ≥ 0·5). The hazard ratio (HR) and 95 % CI for the risk of all-cause mortality related to metabolic obesity phenotypes were calculated using the Cox hazards regression model. During a median follow-up of 6·01 years, 864 deaths were identified. When obesity was defined by WC, the prevalence of participants with metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy non-obesity (MUNO) and metabolically unhealthy obesity (MUO) at baseline was 12·12 %, 2·80 %, 41·93 % and 43·15 %, respectively. After adjusting for age, sex, alcohol drinking, smoking, physical activity and education, the risk of all-cause mortality was higher with both MUNO (HR = 1·20, 95 % CI 1·14, 1·26) and MUO (HR = 1·20, 95 % CI 1·13, 1·27) v. MHNO, but the risk was not statistically significant with MHO (HR = 0·99, 95 % CI 0·89, 1·10). This result remained consistent when stratified by sex. Defining obesity by WHtR gave similar results. MHO does not suggest a greater risk of all-cause mortality compared to MHNO, but participants with metabolic abnormality, with or without obesity, have a higher risk of all-cause mortality. These results should be cautiously interpreted as the representation of MHO is small.

The goal of few-shot semantic segmentation is to learn a segmentation model that can segment novel classes in queries when only a few annotated support examples are available. Due to large intra-class variations, the building of accurate semantic correlation remains a challenging job. Current methods typically use 4D kernels to learn the semantic correlation of feature maps. However, they still face the challenge of reducing the consumption of computation and memory while keeping the availability of correlations mined by their methods. In this paper, we propose the adaptively mining correlation network (AMCNet) to alleviate the aforementioned issues. The key points of AMCNet are the proposed adaptive separable 4D kernel and the learnable pyramid correlation module, which form the basic block for correlation encoder and provide a learnable concatenation operation over pyramid correlation tensors, respectively. Experiments on the PASCAL VOC 2012 dataset show that our AMCNet surpasses the state-of-the-art method by $0.7\%$ and $2.2\%$ on 1-shot and 5-shot segmentation scenarios, respectively.