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.

In small-plot experiments, weed scientists have traditionally estimated herbicide efficacy through visual assessments or manual counts with wooden frames—methods that are time-consuming, labor-intensive, and error-prone. This study introduces a novel mobile application (app) powered by convolutional neural networks (CNNs) to automate the evaluation of weed coverage in turfgrass. The mobile app automatically segments input images into 10 by 10 grid cells. A comparative analysis of EfficientNet, MobileNetV3, MobileOne, ResNet, ResNeXt, ShuffleNetV1, and ShuffleNetV2 was conducted to identify weed-infested grid cells and calculate weed coverage in bahiagrass (Paspalum notatum Flueggé), dormant bermudagrass [Cynodon dactylon (L.) Pers.], and perennial ryegrass (Lolium perenne L.). Results showed that EfficientNet and MobileOne outperformed other models in detecting weeds growing in bahiagrass, achieving an F1 score of 0.988. For dormant bermudagrass, ResNet performed best, with an F1 score of 0.996. Additionally, app-based coverage estimates (11%) were highly consistent with manual assessments (11%), showing no significant difference (P = 0.3560). Similarly, ResNeXt achieved the highest F1 score of 0.996 for detecting weeds growing in perennial ryegrass, with app-based and manual coverage estimates also closely aligned at 10% (P = 0.1340). High F1 scores across all turfgrass types demonstrate the models’ ability to accurately replicate manual assessments, which is essential for herbicide efficacy trials requiring precise weed coverage data. Moreover, the time for weed assessment was compared, revealing that manual counting with 10 by 10 wooden frames took an average of 39.25, 37.25, and 42.25 s per instance for bahiagrass, dormant bermudagrass, and perennial ryegrass, respectively, whereas the app-based approach reduced the assessment times to 8.23, 7.75, and 14.96 s, respectively. These results highlight the potential of deep learning–based mobile tools for fast, accurate, scalable weed coverage assessments, enabling efficient herbicide trials and offering labor and cost savings for researchers and turfgrass managers.

The remote center of motion (RCM) mechanism is one of the key components of minimally invasive surgical robots. Nevertheless, the most widely used parallelogram-based RCM mechanism tends to have a large footprint, thereby increasing the risk of collisions between the robotic arms during surgical procedures. To solve this problem, this study proposes a compact RCM mechanism based on the coupling of three rotational motions realized by nonlinear transmission. Compared to the parallelogram-based RCM mechanism, the proposed design offers a smaller footprint, thereby reducing the risk of collisions between the robotic arms. To address the possible errors caused by the elasticity of the transmission belts, an error model is established for the transmission structure that includes both circular and non-circular pulleys. A prototype is developed to verify the feasibility of the proposed mechanism, whose footprint is further compared with that of the parallelogram-based RCM mechanism. The results indicate that our mechanism satisfies the constraints of minimally invasive surgery, provides sufficient stiffness, and exhibits a more compact design. The current study provides a new direction for the miniaturization design of robotic arms in minimally invasive surgical robots.

In this paper, we propose a novel online informative path planner for 3-D modeling of unknown structures using micro aerial vehicles. Different from the explore-then-exploit strategy, our planner can cope with exploration and coverage simultaneously and thus obtain complete and high-quality 3-D models. We first devise a set of evaluation metrics considering the perception constraints of the sensor for efficiently evaluating the coverage quality of the reconstructed surfaces. Then, the coverage quality is utilized to guide the subsequent informative path planning. Specifically, our hierarchical planner consists of two planning stages – a local coverage stage for inspecting surfaces with low coverage quality and a global exploration stage for transiting the robot to unexplored regions at the global scale. The local coverage stage computes the coverage path that takes into account both the exploration and coverage objectives based on the estimated coverage quality and frontiers, and the global exploration stage maintains a sparse roadmap in the explored space to achieve fast global exploration. We conduct both simulated and real-world experiments to validate the proposed method. The results show that our planner outperforms the state-of-the-art algorithms and especially decreases the reconstruction error (at least 12.5% lower on average).

Yiyang Dahegu rice (YyDHG) is an important agricultural specialty of Yiyang County, Jiangxi Province, and it is also a significant component of the local cultural and economic development. In this experiment, 89 samples of Dahegu rice (DHG) were collected from Jiangxi Province, including 52 samples of YyDHG and 37 samples of DHG from other regions within Jiangxi Province (oDHG). Comprehensive analysis was conducted using polyacrylamide gel electrophoresis, field phenotypic observation, population structure analysis and quality analysis. The results of variety identification indicated that the 89 samples actually comprised 52 distinct varieties, including 19 varieties of YyDHG. Population analysis has revealed rich genetic diversity among DHG varieties within Jiangxi Province, yet no significant subpopulation differentiation was observed between YyDHG and oDHG. Quality experiments demonstrated that YyDHG exhibits significant differences in appearance quality from oDHG, but no notable differences in milling quality or cooked taste and flavour. This suggests that the competitiveness of YyDHG in the market may not entirely depend on its unique quality characteristics, but rather more on its cultural value and brand effect. This experiment conducted a comprehensive analysis of the variety characteristics, genetic diversity and quality traits of YyDHG. Not only does it provide a scientific basis for the breeding and germplasm resource conservation of YyDHG, but it also holds positive implications for promoting the development of its industry.

Omega-3 Polyunsaturated Fatty Acids (n-3 PUFAs), including alpha-linolenic acid (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA), are widely found in plant oils and marine organisms. These fatty acids demonstrate significant biological effects, and their adequate intake is essential for maintaining health. However, modern diets often lack sufficient n-3 PUFAs, especially among populations that consume little fish or seafood,leading to a growing interest in n-3 PUFAs supplementation in nutrition and health research. In recent decades, the role of n-3 PUFAs in preventing and treating various diseases has gained increasing attention, particularly in cardiovascular, neurological, ophthalmic, allergic, hepatic, and oncological fields.In orthopedics, n-3 PUFAs exert beneficial effects through several mechanisms, including modulation of inflammatory responses, enhancement of cartilage repair, and regulation of bone metabolism. These effects demonstrate potential for the treatment of conditions such as osteoarthritis (OA), rheumatoid arthritis (RA), gout, osteoporosis (OP), fractures, sarcopenia, and spinal degenerative diseases (SDD). This review summarizes the clinical applications of n-3 PUFAs, with a focus on their research progress in the field of orthopedics, and explores their potential in the treatment of orthopedic diseases.

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.

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.

Automatic precision herbicide application offers significant potential for reducing herbicide use in turfgrass weed management. However, developing accurate and reliable neural network models is crucial for achieving optimal precision weed control. The reported neural network models in previous research have been limited by specific geographic regions, weed species, and turfgrass management practices, restricting their broader applicability. The objective of this research was to evaluate the feasibility of deploying a single, robust model for weed classification across a diverse range of weed species, considering variations in species, ecotypes, densities, and growth stages in bermudagrass turfgrass systems across different regions in both China and the United States. Among the models tested, ResNeXt152 emerged as the top performer, demonstrating strong weed detection capabilities across 24 geographic locations and effectively identifying 14 weed species under varied conditions. Notably, the ResNeXt152 model achieved an F1 score and recall exceeding 0.99 across multiple testing scenarios, with a Matthews correlation coefficient (MCC) value surpassing 0.98, indicating its high effectiveness and reliability. These findings suggest that a single neural network model can reliably detect a wide range of weed species in diverse turf regimes, significantly reducing the costs associated with model training and confirming the feasibility of using one model for precision weed control across different turf settings and broad geographic regions.

Language comprehension requires integration of multiple cues, but the underlying mechanisms of how accentuation, as a significant prosodic feature, influences the processing of words with different levels of cloze probability remains unclear. This study exploits event-related potentials (ERPs) to examine the processing of accented and unaccented words with high-, medium-, and low-cloze probabilities embedded in the final position of highly constrained contexts during spoken sentence comprehension. Our results indicate that accentuation and cloze probability interact across the N400 and post-N400 positivity (PNP) time windows. Under the accented condition, N400 amplitudes gradually increased as cloze probability decreased. Conversely, under the unaccented condition, PNP amplitudes gradually increased as cloze probability decreased with a frontal distribution. These results suggest that the effect of predictability is influenced by accentuation, which is likely due to the processing speed and depth of the critical words, modulated by the amount of attentional resources allocated to them.