American silk moth, Antheraea polyphemus Cramer 1775 (Lepidoptera: Saturniidae), native to North America, has potential significance in sericulture for food consumption and silk production. To date, the phylogenetic relationship and divergence time of A. polyphemus with its Asian relatives remain unknown. To end these issues, two mitochondrial genomes (mitogenomes) of A. polyphemus from the USA and Canada respectively were determined. The mitogenomes of A. polyphemus from the USA and Canada were 15,346 and 15,345 bp in size, respectively, with only two transitions and five indels. The two mitogenomes both encoded typical mitochondrial 37 genes. No tandem repeat elements were identified in the A+T-rich region of A. polyphemus. The mitogenome-based phylogenetic analyses supported the placement of A. polyphemus within the genus Antheraea, and revealed the presence of two clades for eight Antheraea species used: one included A. polyphemus, A. assamensis Helfer, A. formosana Sonan and the other contained A. mylitta Drury, A. frithi Bouvier, A. yamamai Guérin-Méneville, A. proylei Jolly, and A. pernyi Guérin-Méneville. Mitogenome-based divergence time estimation further suggested that the dispersal of A. polyphemus from Asia into North America might have occurred during the Miocene Epoch (18.18 million years ago) across the Berling land bridge. This study reports the mitogenome of A. polyphemus that provides new insights into the phylogenetic relationship among Antheraea species and the origin of A. polyphemus.

The human hand’s exceptional dexterity and compliance, derived from its rigid-soft coupling structure and tendon-driven interphalangeal coordination, inspire robotic grippers capable of versatile grasping and force adaptation. Traditional rigid manipulators lack compliance for delicate tasks, while soft robots often suffer from instability and low load capacity. To bridge this gap, we propose a biomimetic multi-joint composite finger integrating a 3D-printed rigid phalanges (46–51 mm) with dual fabric-reinforced pneumatic bladders, mimicking human finger biomechanics. This hybrid design combines hinge-jointed rigidity and anisotropic fabric constraints, enabling two rotational degrees of freedom with higher radial stiffness, achieving 2.18× higher critical burst pressure (240 kPa) than non-reinforced bladders, while preserving axial compliance. Experimental validation demonstrates a 4.77 N maximum fingertip force at 200 kPa and rapid recovery (< 2s) post-impact. The composite finger exhibits human-like gestures (enveloping, pinching, flipping) and adapts to irregular/fragile objects (e.g., eggs, screws) through coordinated bladder actuation. Assembled into a modular gripper, it sustains 1 kg payloads and executes thin-object flipping via proximal-distal joint synergy. This rigid-soft coupling design bridges compliance and robustness, offering high environmental adaptability for applications in industrial automation, human–robot interaction, and delicate manipulation.

Rayleigh–Taylor instability (RTI) caused by rarefaction waves not only features variable acceleration but also incorporates time-dependent density, which introduces great challenges in predicting the finger growth behaviours. In this work, we propose a model for predicting the single-mode finger behaviours by extending the Layzer potential-flow framework to account for time-dependent acceleration and density. Relative to the previous models, the present model can evaluate the effect of time-dependent density on finger growth, and can describe the growth behaviours of both bubbles and spikes in rarefaction-driven RTI flows. In addition, the time-dependent curvature of the finger tip as it evolves from its initial value to the quasi-steady value is quantified. To validate the model, rarefaction-tube experiments and numerical simulations are conducted across a wide range of initial conditions. The results show that the present model can accurately capture the amplitude growth and curvature evolution of bubbles and spikes across various density ratios. Moreover, both the present model and experiments demonstrate that the continuous density reduction in rarefaction-driven flows causes larger asymptotic velocities of bubbles and spikes, leading to higher Froude numbers relative to those under constant or time-dependent acceleration.

Cryphodera guangdongensis n. sp. was collected from the soil and roots of Schima superba in Guangdong province, China. The new species is characterised by having a nearly spherical female, with dimensions of length × width = 532.3 (423.8–675.3) × 295.6 (160.0–381.2) μm, stylet length of 35.7 (31.1–42.1) μm, protruding vulval lips, a vulval slit measuring 54.2 (47.4–58.9) μm, an area between the vulva and anus that is flat to concave, and a vulva–anus distance 49.3 (41.1–57.6) μm. The male features two lip annules, a stylet length of 31.7 (27.4–34.8) μm and basal knobs that are slightly projecting anteriorly, while lateral field is areolated with three incisures and spicules length of 27.1 (23.7–31.0) μm. The second stage juvenile is characterised by a body length of 506.1 (441.8–564.4) μm long, two to three lip annules, a stylet length 31.2 (29.7–33.2) μm which is well developed, basal knobs projecting anteriorly, a lateral field that is areolate with three incisures, and a narrow rounded tail measuring 63.2 (54.2–71.3) μm long, with a hyaline region of 35.6 (27.4–56.6) μm long that is longer than the stylet. Based on morphology and morphometrics, the new species is closely related to C. sinensis and C. japonicum within the genus Cryphodera. The phylogenetic trees constructed based on the ITS-rRNA, 28S-rRNA D2–D3 region, and the partial COI gene sequences indicate that the new species clusters with other Cryphodera species but maintains in a separated subgroup. A key to the species of the genus Cryphodera is also provided in this study.

The interaction of helminth infections with type 2 diabetes (T2D) has been a major area of research in the past few years. This paper, therefore, focuses on the systematic review of the effects of helminthic infections on metabolism and immune regulation related to T2D, with mechanisms through which both direct and indirect effects are mediated. Specifically, the possible therapeutic role of helminths in T2D management, probably mediated through the modulation of host metabolic pathways and immune responses, is of special interest. This paper discusses the current possibilities for translating helminth therapy from basic laboratory research to clinical application, as well as existing and future challenges. Although preliminary studies suggest the potential for helminth therapy for T2D patients, their safety and efficacy still need to be confirmed by larger-scale clinical studies.

Wall cooling is a promising method in controlling compressible flows, including hypersonic shock wave turbulent boundary layer interaction (STBLI). Based on the verified DNS method, a 30-degree compression ramp is used to generate STBLI for Ma of 5 and wall to recovery temperature ratio ranging from 0.2 to 1.0. The results indicate that the separation zone decreases for cold wall conditions and quantitatively validate the wall-temperature-corrected interaction scaling theory in recent literature. The heat transfer results show that the wall cooling greatly influences the heat flux distribution and peak values in STBLI. The two-stage heat flux increase disappears for the cold wall, which corresponds to the reduced separation bubble. The local decrease of the recovery temperature is observed after the shock, which causes the negative heat flux minimum for near ‘adiabatic’ wall conditions and can be attributed to the acceleration of the near-wall supersonic fluid in the turning process. On the whole, the decrease of the wall temperature leads to the 24.3% decrease of the peak heat flux enhancement, and the underlying mechanism is the decrease of the near-wall turbulent aerodynamic heat dissipation enhancement for the wall cooling.

To address the problems of accuracy degradation, localization drift, and even failure of Simultaneous Localization and Mapping (SLAM) algorithms in unstructured environments with sparse geometric features, such as outdoor parks, highways, and urban roads, a multi-metric light detection and ranging (LiDAR) SLAM system based on the fusion of geometric and intensity features is proposed. Firstly, an adaptive method for extracting multiple types of geometric features and salient intensity features is proposed to address the issue of insufficient sparse feature extraction. In addition to extracting traditional edge and planar features, vertex features are also extracted to fully utilize the geometric information, and intensity edge features are extracted in areas with significant intensity changes to increase multi-level perception of the environment. Secondly, in the state estimation, a multi-metric error estimation method based on point-to-point, point-to-line, and point-to-plane is used, and a two-step decoupling strategy is employed to enhance pose estimation accuracy. Finally, qualitative and quantitative experiments on public datasets demonstrate that compared to state-of-the-art pure geometric and intensity-assisted LiDAR SLAM algorithms, our proposed algorithm achieves superior localization accuracy and mapping clarity, with an ATE accuracy improvement of 28.93% and real-time performance of up to 62.9 ms. Additionally, test conducted in real campus environments further validates the effectiveness of our approach in complex, unstructured scenarios.

Rayleigh–Taylor (RT) stability occurs when a single-mode light/heavy interface is accelerated by rarefaction waves, exhibiting a sustained oscillation in perturbation amplitude. If the perturbation is accelerated again by a shock propagating in the same direction as the rarefaction waves, the interface evolution will shift from RT stability to Richtmyer–Meshkov (RM) instability. Depending upon the interface state when the shock arrives, the perturbation growth can be actively manipulated through controlling the magnitudes of vorticity deposited by rarefaction and shock waves. The present work first theoretically analyses the 12 different growth possibilities of a light/heavy interface accelerated by co-directional rarefaction and shock waves. A theoretical model is established by combining the RT growth rate with the RM growth rate, providing the conditions for the different possibilities of the perturbation growth. Based on the model, extensive experiments are designed and conducted in the specially designed rarefaction-shock tube. By precisely controlling the shock arrival time at the interface, the different growth possibilities, including promotion, reduction and freeze-out, are realised in experiments. This work verifies the feasibility of manipulating the light/heavy perturbation via co-directional rarefaction and shock waves, which sheds light on control of hydrodynamic instabilities in practical applications.

Persistent malnutrition is associated with poor clinical outcomes in cancer. However, assessing its reversibility can be challenging. The present study aimed to utilise machine learning (ML) to predict reversible malnutrition (RM) in patients with cancer. A multicentre cohort study including hospitalised oncology patients. Malnutrition was diagnosed using an international consensus. RM was defined as a positive diagnosis of malnutrition upon patient admission which turned negative one month later. Time-series data on body weight and skeletal muscle were modelled using a long short-term memory architecture to predict RM. The model was named as WAL-net, and its performance, explainability, clinical relevance and generalisability were evaluated. We investigated 4254 patients with cancer-associated malnutrition (discovery set = 2977, test set = 1277). There were 2783 men and 1471 women (median age = 61 years). RM was identified in 754 (17·7 %) patients. RM/non-RM groups showed distinct patterns of weight and muscle dynamics, and RM was negatively correlated to the progressive stages of cancer cachexia (r = –0·340, P < 0·001). WAL-net was the state-of-the-art model among all ML algorithms evaluated, demonstrating favourable performance to predict RM in the test set (AUC = 0·924, 95 % CI = 0·904, 0·944) and an external validation set (n 798, AUC = 0·909, 95 % CI = 0·876, 0·943). Model-predicted RM using baseline information was associated with lower future risks of underweight, sarcopenia, performance status decline and progression of malnutrition (all P < 0·05). This study presents an explainable deep learning model, the WAL-net, for early identification of RM in patients with cancer. These findings might help the management of cancer-associated malnutrition to optimise patient outcomes in multidisciplinary cancer care.

Milk fat is a crucial component for evaluating the production performance and nutritional value of goat milk. Previous research indicated that the composition of ruminal microbiota plays a significant role in regulating milk fat percentage in ruminants. Thus, this study aimed to identify key ruminal microorganisms and blood metabolites relevant to milk fat synthesis in dairy goats as a mean to explore their role in regulating milk fat synthesis. Sixty clinically healthy Xinong Saanen dairy goats at mid-lactation and of similar body weight, and similar milk yield were used in a feeding study for 15 days. Based on daily milk yield of dairy goats and the results of milk component determination on the 1st and 8th days, five goats with the highest milk fat content (H group) and five goats with the lowest milk fat content (L group) were selected for further analysis. Before the morning feeding on the 15th day of the experiment, samples of milk, blood and ruminal fluid were collected for analyses of components, volatile fatty acids, microbiota and metabolites. Results revealed that acetate content in the rumen of H group was greater compared with L group. H group had abundant beneficial bacteria including Ruminococcaceae_UCG-005, Saccharofermentans, Ruminococcaceae-UCG-002 and Prevotellaceae_UCG-3, which were important for plant cellulose and hemicellulose degradation and immune regulation. Metabolomics analysis revealed H group had greater relative concentrations of 4-acetamidobutanoic acid and azelaic acid in serum, and had lower relative concentrations of Arginyl-Alanine, SM(d18:1/12:0) and DL-Tryptophan. These altered metabolites are involved in the sphingolipid signaling pathway, arginine and proline metabolism. Overall, this study identified key ruminal microorganisms and serum metabolites associated with milk fat synthesis in dairy goats. These findings offer insights for enhancing the quality of goat milk and contribute to a better understanding of the regulatory mechanisms involved in milk fat synthesis in dairy goats.

This study was designed to explore changes in soil bulk density (BD), soil organic carbon (SOC) content, SOC stocks, and soil labile organic carbon (C) fractions after 5 years of soil tillage management under the double-cropping rice system in southern of China. The experiment included four soil tillage treatments: rotary tillage with all crop residues removed as a control (RTO); conventional tillage with crop residues incorporation (CT); rotary tillage with crop residues incorporation (RT); and no-tillage with crop residues retention. Our results revealed that soil tillage combined with crop residue incorporation (CT and RT) significantly decreased BD at 0–20 cm soil layer compared to RTO treatment. SOC content and stocks were increased with the application of crop residues. Compared with RTO treatment, SOC content and stocks were increased by 16.8% and 9.8% in CT treatment, respectively. Soil non-labile C content and proportion of labile C were increased due to crop residue incorporation. Compared with RTO treatment, soil proportion of C mineralisation (Cmin), permanganate oxidisable C (KMnO4), particulate organic C (POC), and microbial biomass C (MBC) was increased by 196.1%, 41.4%, 31.4%, and 17.1% under CT treatment, respectively. These results were confirmed by the carbon management index, which was significantly increased under soil tillage with crop residue incorporation. Here, we demonstrated that soil tillage and crop residue incorporation can increase the pool of stable C at surface soil layer while increasing labile C content and proportion. In conclusion, conventional or rotary tillage combined with crop residue incorporation is a soil management able to improve nutrient cycling and soil quality in paddy fields in southern China.

Multi-loop coupling mechanisms (MCMs) are extensively utilized in the aerospace and aviation industries. This paper analyzes the mobility, singularity, and optimal actuation selection of a 3RR-3RRR MCM on the basis of geometric algebra (GA), where R denotes revolute joint. First, the principle of the shortest path is employed to identify the basic limbs and ascertain the type of coupling limbs. The analytical expression for the twist space and mobility characteristics of the mechanism is obtained by calculating the intersection of the limb’s twist space. The blade of limb constraint is subsequently employed to construct the singular polynomials of the mechanism. The singular configurations of the 3RR-3RRR MCM are analyzed in accordance with the properties of the outer product, resulting in the identification of two distinct types of boundary singularities. Next, the local transmission index is employed to evaluate the motion/force transmission performance of the two actuation schemes and finalize the selection of the superior actuation scheme for the mechanism. Finally, a prototype is developed to evaluate the energy loss resulting from the two actuation schemes, which verifies the correctness of the actuation selection scheme.

Emission line galaxies (ELGs) are crucial for cosmological studies, particularly in understanding the large-scale structure of the Universe and the role of dark energy. ELGs form an essential component of the target catalogue for the Dark Energy Spectroscopic Instrument (DESI), a major astronomical survey. However, the accurate selection of ELGs for such surveys is challenging due to the inherent uncertainties in determining their redshifts with photometric data. In order to improve the accuracy of photometric redshift estimation for ELGs, we propose a novel approach CNN–MLP that combines convolutional neural networks (CNNs) with multilayer perceptrons (MLPs). This approach integrates both images and photometric data derived from the DESI Legacy Imaging Surveys Data Release 10. By leveraging the complementary strengths of CNNs (for image data processing) and MLPs (for photometric feature integration), the CNN–MLP model achieves a $\sigma_{\mathrm{NMAD}}$ (normalised median absolute deviation) of 0.0140 and an outlier fraction of 2.57%. Compared to other models, CNN–MLP demonstrates a significant improvement in the accuracy of ELG photometric redshift estimation, which directly benefits the target selection process for DESI. In addition, we explore the photometric redshifts of different galaxy types (Starforming, Starburst, AGN, and Broadline). Furthermore, this approach will contribute to more reliable photometric redshift estimation in ongoing and future large-scale sky surveys (e.g. LSST, CSST, and Euclid), enhancing the overall efficiency of cosmological research and galaxy surveys.

Germplasm resources are the foundation for improving crop varieties and a strategic asset for global food security. They also advance plant breeding, agricultural biotechnology and the production of essential agricultural goods. To assess the distribution, diversity and conservation status of food crop germplasm in the Hainan Province, China, we conducted a detailed survey of the Hainan Island. Between 2017 and 2022, we collected 330 food crop germplasm resources, encompassing 16 cereal crops, including rice, maize, sweet potato. The collected germplasm resources exhibited traits of high resistance to both biotic and abiotic stresses, including common diseases and drought stress, as well as superior quality and adaptability to poor soil conditions such as sandy land. However, challenges such as low productivity and hybrid degradation were identified. These resources were primarily found in Haikou City, Baisha County, Danzhou City, Wuzhishan City and Sanya City. Additionally, we collected several ancient local varieties and endangered germplasm resources such as ‘Jiezi rice’ and ‘Wuzhishan maize’. This study serves as a reference for the conservation, development and utilization of local food crop germplasm resources in Hainan Province and lays the foundation for breeding and developing new varieties.