The sulphur microbial diet (SMD), a dietary pattern associated with forty-three sulphur-metabolising bacteria, may influence gut microbiota composition and contribute to ageing process through gut-produced hydrogen sulfide (H2S). We aimed to explore the association between SMD and biological age (BA) acceleration, using the cross-sectional study that included 71 579 individuals from the UK Biobank. The SMD score was calculated by multiplying β-coefficients by corresponding serving sizes and summing them, based on dietary data collected using the Oxford WebQ, a 24-hour dietary assessment tool. BA was assessed using Klemerae–Doubal (KDM) and PhenoAge methods. The difference between BA and chronological age refers to the age acceleration (AgeAccel), termed ‘KDMAccel’ and ‘PhenoAgeAccel’. Generalised linear regression was performed. Mediation analyses were used to investigate underlying mediators including BMI and serum aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio. Following adjustment for multiple variables, a positive association was observed between consuming a dietary pattern with a higher SMD score and both KDMAccel (βQ4 v. Q1 = 0·35, 95 % CI = 0·27, 0·44, P < 0·001) and PhenoAgeAccel (βQ4 v. Q1 = 0·32, 95 % CI = 0·23, 0·41, P < 0·001). Each 1-SD increase in SMD score was positively associated with the acceleration of BA by 7·90 % for KDMAccel (P < 0·001) and 7·80 % for PhenoAgeAccel (P < 0·001). BMI and AST/ALT mediated the association. The stratified analysis revealed stronger accelerated ageing impacts in males and smokers. Our study indicated a higher SMD score is associated with elevated markers of biological ageing, supporting the potential utility of gut microbiota-targeted dietary interventions in attenuating the ageing process.

In laser systems requiring a flat-top distribution of beam intensity, beam smoothing is a critical technology for enhancing laser energy deposition onto the focal spot. The continuous phase modulator (CPM) is a key component in beam smoothing, as it introduces high-frequency continuous phase modulation across the laser beam profile. However, the presence of the CPM makes it challenging to measure and correct the wavefront aberration of the input laser beam effectively, leading to unwanted beam intensity distribution and bringing difficulty to the design of the CPM. To address this issue, we propose a deep learning enabled robust wavefront sensing (DLWS) method to achieve effective wavefront measurement and active aberration correction, thereby facilitating active beam smoothing using the CPM. The experimental results show that the average wavefront reconstruction error of the DLWS method is 0.04 μm in the root mean square, while the Shack–Hartmann wavefront sensor reconstruction error is 0.17 μm.

A multifunctional optical diagnostic system, which includes an interferometer, a refractometer and a multi-frame shadowgraph, has been developed at the Shenguang-II upgrade laser facility to characterize underdense plasmas in experiments of the double-cone ignition scheme of inertial confinement fusion. The system employs a 266 nm laser as the probe to minimize the refraction effect and allows for flexible switching among three modes of the interferometer, refractometer and multi-frame shadowgraph. The multifunctional module comprises a pair of beam splitters that attenuate the laser, shield stray light and configure the multi-frame and interferometric modules. By adjusting the distance and angle between the beam splitters, the system can be easily adjusted and switched between the modes. Diagnostic results demonstrate that the interferometer can reconstruct electron density below 1019 cm–3, while the refractometer can diagnose density approximately up to 1020 cm–3. The multi-frame shadowgraph is used to qualitatively characterize the temporal evolution of plasmas in the cases in which the interferometer and refractometer become ineffective.

The aeroelasticity of a panel in the presence of a shock is a fundamental issue of great significance in the development of hypersonic vehicles. In practical engineering, cavity pressure emerges as a crucial factor that influences the nonlinear dynamical characteristics of the panel. This study focuses on the aeroelastic bifurcation of a flexible panel subjected to both cavity pressure and oblique shock. To this end, a computational method is devised, coupling a high-fidelity reduced-order model for unsteady aerodynamic loads with nonlinear structural equations. The solution is meticulously tracked by continuous calculations. The obtained results indicate that cavity pressure plays a pivotal role in determining the bifurcation and stability characteristics of the system. First, the system exhibits hysteresis behaviour in response to the ascending and descending dynamic pressures. The evolution of hysteresis behaviour originates from the phenomenon of cusp catastrophe. Second, variations in cavity pressure induce three types of bifurcation phenomena, exhibiting characteristics akin to supercritical Hopf bifurcation, subcritical Hopf bifurcation and saddle-node bifurcation of cycles. The system's response at the critical points of these bifurcations manifests as long-period asymptotic flutter or explosive flutter. Lastly, the evolution of the dynamical system among these three types of bifurcations is an important factor contributing to the discrepancies observed in certain research results. This study enhances the understanding of the nonlinear dynamical behaviour of panel aeroelasticity in complex practical environments and provides new explanations for the discrepancies observed in certain research results.

Robots with multi-sensors always have a problem of weak pairing among different modals of the collected information produced by multi-sensors, which leads to a bad perception performance during robot interaction. To solve this problem, this paper proposes a Force Vision Sight (FVSight) sensor, which utilizes a distributed flexible tactile sensing array integrated with a vision unit. This innovative approach aims to enhance the overall perceptual capabilities for object recognition. The core idea is using one perceptual layer to trigger both tactile images and force-tactile arrays. It allows the two heterogeneous tactile modal information to be consistent in the temporal and spatial dimensions, thus solving the problem of weak pairing between visual and tactile data. Two experiments are specially designed, namely object classification and slip detection. A dataset containing 27 objects with deep presses and shallow presses is collected for classification, and then 20 slip experiments on three objects are conducted. The determination of slip and stationary state is accurately obtained by covariance operation on the tactile data. The experimental results show the reliability of generated multimodal data and the effectiveness of our proposed FVSight sensor.

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.

Systolic blood pressure (SBP) is significantly associated with body composition in children and adolescents. However, which one of the components of body composition is the dominant contributor to SBP in children and adolescents remains unclear. We, therefore, aimed to determine the dominant contributor to SBP among components of body composition in a large cohort of American children and adolescents derived from the National Health and Nutrition Examination Survey with cross-sectional analysis. In total, 13 618 children and adolescents (median age 13 years; 6107 girls) with available data on whole-body dual-emission X-ray absorptiometry measurements were included. Multiple linear regression showed that SBP was associated with higher total fat-free mass in boys (β = 0·49, P < 0·001) and girls (β = 0·47, P < 0·001) and with higher total fat mass only in boys (β = 0·12, P < 0·001) after adjustment for covariates. When taking fat distribution into consideration, SBP was associated with higher trunk fat mass (boys: β = 0·28, P < 0·001; girls: β = 0·15, P < 0·001) but negatively associated with leg fat mass (Boys: β = −0·14, P < 0·001; Girls: β = −0·11, P < 0·001), in both boys and girls. Dominance analysis showed that total fat-free mass was the dominant contributor to SBP (boys: 49 %; girls: 55·3 %), followed by trunk fat mass (boys: 32·1 %; girls: 26·9 %); leg fat mass contributed the least to SBP in boys (18·9 %) and girls (17·8 %). Our findings indicated that total fat-free mass was not only associated with SBP but also the most dominant contributor to SBP variation in American children and adolescents.

3D object detection using point cloud is an essential task for autonomous driving. With the development of infrastructures, roadside perception can extend the view range of the autonomous vehicles through communication technology. Computation time and power consumption are two main concerns when deploying object detection tasks, and a light-weighted detection model applied in an embedded system is a convenient solution for both roadside and vehicleside. In this study, a 3D Point cLoud Object deTection (PLOT) network is proposed to reduce heavy computing and ensure real-time object detection performance in an embedded system. First, a bird’s eye view representation of the point cloud is calculated using pillar-based encoding method. Then a cross-stage partial network-based backbone and a feature pyramid network-based neck are implemented to generate the high-dimensional feature map. Finally, a multioutput head using a shared convolutional layer is attached to predict classes, bounding boxes, and the orientations of the objects at the same time. Extensive experiments using the Waymo Open Dataset and our own dataset are conducted to demonstrate the accuracy and efficiency of the proposed method.

The mitochondrial genome provides important information for phylogenetic analysis and an understanding of evolutionary origin. In this study, the mitochondrial genomes of Ilisha elongata and Setipinna tenuifilis were sequenced, which are typical circular vertebrate mitochondrial genomes composed of 16,770 and 16,805 bp, respectively. The mitogenomes of I. elongata and S. tenuifilis include 13 protein-coding genes (PCGs), 22 transfer RNA (tRNA), two ribosomal RNA (rRNA) genes and one control region (CR). Both two species' genome compositions were highly A + T biased and exhibited positive AT-skews and negative GC-skews. The genetic distance and Ka/Ks ratio analyses indicated that 13 PCGs were affected by purifying selection and the selection pressures were different from certain deep-sea fishes, which were most likely due to the difference in their living environment. Results of phylogenetic analysis support close relationships among Chirocentridae, Denticipitidae, Clupeidae, Engraulidae and Pristigasteridae based on the nucleotide and amino acid sequences of 13 PCGs. Within Clupeoidei, I. elongata and S. tenuifilis were most closely related to the family Pristigasteridae and Engraulidae, respectively. These results will help to better understand the evolutionary position of Clupeiformes and provide a reference for further phylogenetic research on Clupeiformes species.

We show that there is no nontrivial idempotent in the reduced group $\ell ^p$-operator algebra $B^p_r(F_n)$ of the free group $F_n$ on n generators for each positive integer n.

Hebei Province was affected by two coronavirus disease 2019 (COVID-19) outbreak waves during the period 22 January 2020 through 27 February 2020 (wave 1) and 2 January 2021 through 14 February 2021 (wave 2). To evaluate and compare the epidemiological characteristics, containment delay, cluster events and social activity, as well as non-pharmaceutical interventions of the two COVID-19 outbreak waves, we examined real-time update information on all COVID-19-confirmed cases from a publicly available database. Wave 1 was closely linked with the COVID-19 pandemic in Wuhan, whereas wave 2 was triggered, to a certain extent, by the increasing social activities such as weddings, multi-household gatherings and church events during the slack agricultural period. In wave 2, the epidemic spread undetected in the rural areas, and people living in the rural areas had a higher incidence rate than those living in the urban areas (5.3 vs. 22.0 per 1 000 000). Furthermore, Rt was greater than 1 in the early stage of the two outbreak waves, and decreased substantially after massive non-pharmaceutical interventions were implemented. In China's ‘new-normal’ situation, development of targeted and effective intervention remains key for COVID-19 control in consideration of the potential threat of new coronavirus strains.

Saetaspongia so far cannot be confidently assigned to any class-level crown group. Clarifying its phylogenetic position requires new information provided by more detailed studies of previously described and/or new material. Some sponge fossils with the typical skeletal architecture of Saetaspongia have recently been recognized in the Cambrian (Stage 4) Balang Biota of Guizhou, China, including S. jianhensis new species and S. cf. S. densa. The new taxon is characterized by the following features: spicules are fine monaxons and are inclined to be loosely to densely arranged into plumose arrays; skeleton is composed primarily of one major plumose bundle, with an uncertain number (perhaps two) of small plumose arrays; and primary skeleton is occasionally interspersed with some irregularly oriented individual spicules. An additional specimen consisting of large monaxons, with plumose structures and overlying irregular coarse monaxons, closely fits the description and illustrations of previously described S. cf. S. densa. By combining information from previous studies and the present research, fossil evidence indicates that the plumose architecture is a critical feature diagnostic of Saetaspongia and that there are no hexactine-based spicules in this genus. The new material from the Balang Biota further supports the notion that Saetaspongia has a protomonaxonid rather than a hexactinellid affinity. Fossil evidence suggests that Saetaspongia had a wide biogeographic distribution during the early Cambrian and the stratigraphic distribution of this genus extends up to Stage 4.

UUID: http://zoobank.org/e9ca5e4a-134c-4daa-9266-19085e32614f.

Real-time localization is an important mission for self-driving cars and it is difficult to achieve precise pose information in dynamic environments. In this paper, a novel localization method is proposed to estimate the pose of self-driving cars using a 3D-LiDAR sensor. First, the multi-frame curb features and laser intensity features are extracted. Meanwhile, based on the high-precision curb map generated offline, obstacles on road are detected using region segmentation methods and their features are removed. Furthermore, a map-matching method is proposed to match the features to the map, a robust iterative closest point algorithm is utilized to deal with curb features along with a probability search method dealing with intensity features. Finally, two separate Kalman filters are used to fuse the low-cost global positioning systems and map-matching results. Both offline and online experiments are carried out in dynamic environments and the results demonstrate the accuracy and robustness of the proposed method.

The surface topology of biomaterial has a definite effect on the growth behavior of nerve cells for peripheral nerve regeneration. In this study, the silk fibroin (SF) film with different anisotropic microgroove/ridge was constructed by micropatterning technology. The effects of topologies width on the directional growth of dorsal root ganglion (DRG) neurons were evaluated. The results showed that the topological structure of the SF film with higher SF concentration was more clear and complete. The microtopography of the SF film with a concentration of 15% and a groove width of around 30 μm could effectively guide the directional growth of the nerve fibers of DRG. And nerve fibers could obviously form nerve fiber bundles which may have a certain pavement effect on the recovery of nerve function. The study indicated that the SF film with a specific width of the topological structure may have potential applications in the field of directional nerve regeneration.

Yushu Prefecture in Qinghai Province provides some of the largest known stretches of habitat for the Vulnerable snow leopard Panthera uncia in China. People living in these areas are dependent on agropastoralism. Support from local communities is necessary for effective long-term conservation action for snow leopards, but loss of livestock to snow leopards can create financial burdens that induce negative attitudes and encourage retaliatory killing. We assessed factors driving herders' attitudes towards snow leopards and their conservation. We found that herders had higher agreement with positive than with negative statements about snow leopards despite nearly half reporting livestock loss to snow leopards within the last 5 years. No retaliatory killing was reported. Herders with more years of formal education and fewer livestock losses were more likely to have positive attitudes whereas those with lower importance of snow leopards to their religion, fewer livestock losses, and fewer years of education were more likely to have negative attitudes. Understanding the multifaceted mechanisms responsible for positive views towards species is imperative for reaching conservation goals. Our findings ascribe to the importance of increased education and adherence to Tibetan beliefs in promoting conservation tolerance towards snow leopards in Qinghai Province, but also indicate a need for further research into the impact of livestock loss.