Integrative taxonomy, phylogenetics and historical biogeography of subgenus Aeschyntelus Stål, 1872 (Hemiptera: Heteroptera: Rhopalidae).

The subgenus Aeschyntelus includes six species that show variations in body color and shape, thus making it difficult to identify them based on morphological identification alone. To date, no genetic study has evaluated species within this genus. Herein, we collected 171 individuals from 90 localities of Rhopalus and employed an integrative taxonomic approach that incorporated morphological data, mitochondrial genomic data (COI, whole mitochondrial data) and nuclear genomic data (18S + 28S rRNAs, nuclear genome-wide SNPs) to delineate species boundaries. Our analyses confirmed the status of nine described species of Rhopalus and proposed the recognition of one new species known as Rhopalus qinlinganus sp. nov., which is classified within the subgenus Aeschyntelus. Discrepancies arising from nuclear and mitochondrial data suggest the presence of mito-nuclear discordance. Specifically, mitochondrial data indicated admixture within Clade A, comprising R. kerzhneri and R. latus, whereas genome-wide SNPs unambiguously identified two separate species, aligning with morphological classification. Conversely, mitochondrial data clearly distinguished Clade B- consisting of R. sapporensis into two lineages, whereas genome-wide SNPs unequivocally identified a single species. Our study also provides insights into the evolutionary history of Aeschyntelus, thus indicating that it likely originated in East Asia during the middle Miocene. The development of Aeschyntelus biodiversity in the southwestern mountains of China occurred via an uplift-driven diversification process. Our findings highlight the necessity of integrating both morphological and multiple molecular datasets for precise species identification, particularly when delineating closely related species. Additionally, it reveals the important role of mountain orogenesis on speciation within the southwestern mountains of China.

Data-Driven Design of Triple-Targeted Protein Nanoprobes for Multiplexed Imaging of Cancer Lymphatic Metastasis.

Targeted imaging of cancer lymphatic metastasis remains challenging due to its highly heterogeneous molecular and phenotypic diversity. Herein, triple-targeted protein nanoprobes capable of specifically binding to three targets for imaging cancer lymphatic metastasis, through a data-driven design approach combined with a synthetic biology-based assembly strategy, are introduced. Specifically, to address the diversity of metastatic lymph nodes (LNs), a combination of three targets, including C-X-C motif chemokine receptor 4 (CXCR4), transferrin receptor protein 1 (TfR1), and vascular endothelial growth factor receptor 3 (VEGFR3) is identified, leveraging machine leaning-based bioinformatics analysis and examination of LN tissues from patients with gastric cancer. Using this identified target combination, ferritin nanocage-based nanoprobes capable of specifically binding to all three targets are designed through the self-assembly of genetically engineered ferritin subunits using a synthetic biology approach. Using these nanoprobes, multiplexed imaging of heterogeneous metastatic LNs is successfully achieved in a polyclonal lymphatic metastasis animal model. In 19 freshly resected human gastric specimens, the signal from the triple-targeted nanoprobes significantly differentiates metastatic LNs from benign LNs. This study not only provides an effective nanoprobe for imaging highly heterogeneous lymphatic metastasis but also proposes a potential strategy for guiding the design of targeted nanomedicines for cancer lymphatic metastasis.

Social media unveils the hidden but high magnitude of human-mediated biological invasions in China.

Humans are responsible for the release of many non-native animals into the wild. However, these releases occur randomly and are difficult to monitor. Here, using two of the worst invasive herpetofauna as model taxa, we applied an iEcology approach and found a high magnitude of human-mediated releases in China, suggesting this approach can be used to monitor introductions and advise management bodies in a timely manner.

Meta-analysis reveals less sensitivity of non-native animals than natives to extreme weather worldwide.

Extreme weather events (EWEs; for example, heatwaves, cold spells, storms, floods and droughts) and non-native species invasions are two major threats to global biodiversity and are increasing in both frequency and consequences. Here we synthesize 443 studies and apply multilevel mixed-effects metaregression analyses to compare the responses of 187 non-native and 1,852 native animal species across terrestrial, freshwater and marine ecosystems to different types of EWE. Our results show that marine animals, regardless of whether they are non-native or native, are overall insensitive to EWEs, except for negative effects of heatwaves on native mollusks, corals and anemone. By contrast, terrestrial and freshwater non-native animals are only adversely affected by heatwaves and storms, respectively, whereas native animals negatively respond to heatwaves, cold spells and droughts in terrestrial ecosystems and are vulnerable to most EWEs except cold spells in freshwater ecosystems. On average, non-native animals displayed low abundance in terrestrial ecosystems, and decreased body condition and life history traits in freshwater ecosystems, whereas native animals displayed declines in body condition, life history traits, abundance, distribution and recovery in terrestrial ecosystems, and community structure in freshwater ecosystems. By identifying areas with high overlap between EWEs and EWE-tolerant non-native species, we also provide locations where native biodiversity might be adversely affected by their joint effects and where EWEs might facilitate the establishment and/or spread of non-native species under continuing global change.

Machine-learning-assisted single-vessel analysis of nanoparticle permeability in tumour vasculatures.

The central dogma that nanoparticle delivery to tumours requires enhanced leakiness of vasculatures is a topic of debate. To address this, we propose a single-vessel quantitative analysis method by taking advantage of protein-based nanoprobes and image-segmentation-based machine learning (nano-ISML). Using nano-ISML, >67,000 individual blood vessels from 32 tumour models were quantified, revealing highly heterogenous vascular permeability of protein-based nanoparticles. There was a >13-fold difference in the percentage of high-permeability vessels in different tumours and >100-fold penetration ability in vessels with the highest permeability compared with vessels with the lowest permeability. Our data suggest passive extravasation and transendothelial transport were the dominant mechanisms for high- and low-permeability tumour vessels, respectively. To exemplify the nano-ISML-assisted rational design of nanomedicines, genetically tailored protein nanoparticles with improved transendothelial transport in low-permeability tumours were developed. Our study delineates the heterogeneity of tumour vascular permeability and defines a direction for the rational design of next-generation anticancer nanomedicines.

A low-profile broadband crossed-dipole antenna with fractal structure and inverted-L plates.

A low-profile broadband circularly polarized (CP) crossed-dipole antenna is proposed. In this antenna, the dipole uses a fractal-based structure with multiple similar patches for obtaining broadband CP radiation and impedance matching. The incorporation of the crossed-dipole antenna with four triangular parasitic patches improves both the impedance and axial ratio (AR) bandwidths. Four parasitic coupled inverted-L metal plates are loaded on the ground plane to significantly enhance the AR bandwidth and reduce the antenna profile. The measured results are in agreement with the simulations, which demonstrates that the proposed antenna has a low profile of 0.18 λ0, a - 10 dB impedance bandwidth of 91% (2.78-7.42 GHz), a 3 dB AR bandwidth of 81.5% (2.99-7.1 GHz), and a good right hand circular polarization (RHCP) radiation pattern with an average gain of 7.9 dBi over the whole operating band.

Prediction and Design of Nanozymes using Explainable Machine Learning.

An abundant number of nanomaterials have been discovered to possess enzyme-like catalytic activity, termed nanozymes. It is identified that a variety of internal and external factors influence the catalytic activity of nanozymes. However, there is a lack of essential methodologies to uncover the hidden mechanisms between nanozyme features and enzyme-like activity. Here, a data-driven approach is demonstrated that utilizes machine-learning algorithms to understand particle-property relationships, allowing for classification and quantitative predictions of enzyme-like activity exhibited by nanozymes. High consistency between predicted outputs and the observations is confirmed by accuracy (90.6%) and R2 (up to 0.80). Furthermore, sensitive analysis of the models reveals the central roles of transition metals in determining nanozyme activity. As an example, the models are successfully applied to predict or design desirable nanozymes by uncovering the hidden relationship between different periods of transition metals and their enzyme-like performance. This study offers a promising strategy to develop nanozymes with desirable catalytic activity and demonstrates the potential of machine learning within the field of material science.

Design and Application of Highly Efficient Flame Retardants for Polycarbonate Combining the Advantages of Cyclotriphosphazene and Silicone Oil.

A novel flame retardant (HSPCTP) was successfully designed and incorporated into a polycarbonate (PC) matrix. Combining the advantages of cyclotriphosphazene and silicone oil, PC/HSPCTP composites passed UL-94 V-0 rating testing with only 3 wt% HSPCTP, and their LOI value increased from 25.0% to 28.4%. The findings showed that HSPCTP exhibits both gas-phase and solid-phase flame-retardant effects. Furthermore, the incorporation of HSPCTP into PC could suppress the release of smoke. Finally, the flame-retardant mechanism is discussed in depth.