HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints. To predict the exogenous HLA class II-restricted peptides across most of the human population, we utilized the mass spectrometry data to profile >223 000 eluted ligands over HLA-DR, -DQ, and -DP alleles. Here, by integrating these data with peptide processing and gene expression, we introduce HLAIImaster, an attention-based deep learning framework with adaptive domain knowledge for predicting neoepitope immunogenicity. Leveraging diverse biological characteristics and our enhanced deep learning framework, HLAIImaster is significantly improved against existing tools in terms of positive predictive value across various neoantigen studies. Robust domain knowledge learning accurately identifies neoepitope immunogenicity, bridging the gap between neoantigen biology and the clinical setting and paving the way for future neoantigen-based therapies to provide greater clinical benefit. In summary, we present a comprehensive exploitation of the immunogenic neoepitope repertoire of cancers, facilitating the effective development of "just-in-time" personalized vaccines.

Cone-Opponent Ganglion Cells in the Primate Fovea Tuned to Noncardinal Color Directions.

A long-standing question in vision science is how the three cone photoreceptor types-long (L), medium (M), and short (S) wavelength sensitive-combine to generate our perception of color. Hue perception can be described along two opponent axes: red-green and blue-yellow. Psychophysical measurements of color appearance indicate that the cone inputs to the red-green and blue-yellow opponent axes are M vs. L + S and L vs. M + S, respectively. However, the "cardinal directions of color space" revealed by psychophysical measurements of color detection thresholds following adaptation are L vs. M and S vs. L + M. These cardinal directions match the most common cone-opponent retinal ganglion cells (RGCs) in the primate retina. Accordingly, the cone opponency necessary for color appearance is thought to be established in the cortex. While neurons with the appropriate M vs. L + S and L vs. M + S opponency have been reported in the retina and lateral geniculate nucleus, their existence continues to be debated. Resolving this long-standing debate is necessary because a complete account of the cone opponency in the retinal output is critical for understanding how downstream neural circuits process color. Here, we performed adaptive optics calcium imaging to noninvasively measure foveal RGC light responses in the living Macaca fascicularis eye. We confirm the presence of L vs. M + S and M vs. L + S neurons with noncardinal cone opponency and demonstrate that cone-opponent signals in the retinal output are more diverse than classically thought.

Multi-modality attribute learning-based method for drug-protein interaction prediction based on deep neural network.

Identification of active candidate compounds for target proteins, also called drug-protein interaction (DPI) prediction, is an essential but time-consuming and expensive step, which leads to fostering the development of drug discovery. In recent years, deep network-based learning methods were frequently proposed in DPIs due to their powerful capability of feature representation. However, the performance of existing DPI methods is still limited by insufficiently labeled pharmacological data and neglected intermolecular information. Therefore, overcoming these difficulties to perfect the performance of DPIs is an urgent challenge for researchers. In this article, we designed an innovative 'multi-modality attributes' learning-based framework for DPIs with molecular transformer and graph convolutional networks, termed, multi-modality attributes (MMA)-DPI. Specifically, intermolecular sub-structural information and chemical semantic representations were extracted through an augmented transformer module from biomedical data. A tri-layer graph convolutional neural network module was applied to associate the neighbor topology information and learn the condensed dimensional features by aggregating a heterogeneous network that contains multiple biological representations of drugs, proteins, diseases and side effects. Then, the learned representations were taken as the input of a fully connected neural network module to further integrate them in molecular and topological space. Finally, the attribute representations were fused with adaptive learning weights to calculate the interaction score for the DPIs tasks. MMA-DPI was evaluated in different experimental conditions and the results demonstrate that the proposed method achieved higher performance than existing state-of-the-art frameworks.

DrugMGR: a deep bioactive molecule binding method to identify compounds targeting proteins.

MOTIVATION: Understanding the intermolecular interactions of ligand-target pairs is key to guiding the optimization of drug research on cancers, which can greatly mitigate overburden workloads for wet labs. Several improved computational methods have been introduced and exhibit promising performance for these identification tasks, but some pitfalls restrict their practical applications: (i) first, existing methods do not sufficiently consider how multigranular molecule representations influence interaction patterns between proteins and compounds; and (ii) second, existing methods seldom explicitly model the binding sites when an interaction occurs to enable better prediction and interpretation, which may lead to unexpected obstacles to biological researchers. RESULTS: To address these issues, we here present DrugMGR, a deep multigranular drug representation model capable of predicting binding affinities and regions for each ligand-target pair. We conduct consistent experiments on three benchmark datasets using existing methods and introduce a new specific dataset to better validate the prediction of binding sites. For practical application, target-specific compound identification tasks are also carried out to validate the capability of real-world compound screen. Moreover, the visualization of some practical interaction scenarios provides interpretable insights from the results of the predictions. The proposed DrugMGR achieves excellent overall performance in these datasets, exhibiting its advantages and merits against state-of-the-art methods. Thus, the downstream task of DrugMGR can be fine-tuned for identifying the potential compounds that target proteins for clinical treatment. AVAILABILITY AND IMPLEMENTATION: https://github.com/lixiaokun2020/DrugMGR.

A Dual-Modal, Label-Free Raman Imaging Method for Rapid Virtual Staining of Large-Area Breast Cancer Tissue Sections.

As one of the most common cancers, accurate, rapid, and simple histopathological diagnosis is very important for breast cancer. Raman imaging is a powerful technique for label-free analysis of tissue composition and histopathology, but it suffers from slow speed when applied to large-area tissue sections. In this study, we propose a dual-modal Raman imaging method that combines Raman mapping data with microscopy bright-field images to achieve virtual staining of breast cancer tissue sections. We validate our method on various breast tissue sections with different morphologies and biomarker expressions and compare it with the golden standard of histopathological methods. The results demonstrate that our method can effectively distinguish various types and components of tissues, and provide staining images comparable to stained tissue sections. Moreover, our method can improve imaging speed by up to 65 times compared to general spontaneous Raman imaging methods. It is simple, fast, and suitable for clinical applications.

Integrated transcriptomic and metabolomic analyses elucidate the mechanism of flavonoid biosynthesis in the regulation of mulberry seed germination under salt stress.

Seed propagation is the main method of mulberry expansion in China, an important economic forest species. However, seed germination is the most sensitive stage to various abiotic stresses, especially salinity stress. To reveal the molecular regulatory mechanism of mulberry seed germination under salt stress, flavonoid metabolomics and transcriptomics analyses were performed on mulberry seeds germinated under 50 and 100 mmol/L NaCl stress. Analysis of the flavonoid metabolome revealed that a total of 145 differential flavonoid metabolites (DFMs) were classified into 9 groups, 40 flavonols, 32 flavones, 16 chalcones and 14 flavanones. Among them, 61.4% (89) of the DFMs accumulated continuously with increasing salt concentration, reaching the highest level at a 100 mmol/L salt concentration; these DFMs included quercetin-3-O-glucoside (isoquercitrin), kaempferol (3,5,7,4'-tetrahydroxyflavone), quercetin-7-O-glucoside, taxifolin (dihydroquercetin) and apigenin (4',5,7-trihydroxyflavone), indicating that these flavonoids may be key metabolites involved in the response to salt stress. Transcriptional analysis identified a total of 3055 differentially expressed genes (DEGs), most of which were enriched in flavonoid biosynthesis (ko00941), phenylpropanoid biosynthesis (ko00940) and biosynthesis of secondary metabolites (ko01110). Combined analysis of flavonoid metabolomic and transcriptomic data indicated that phenylalanine ammonia-lyase (PAL), 4-coumarate-CoA ligase (4CL), chalcone synthase (CHS), flavonol synthase (FLS), bifunctional dihydroflavonol 4-reductase/flavanone 4-reductase (DFR) and anthocyanidin reductase (ANR) were the key genes involved in flavonoid accumulation during mulberry seed germination under 50 and 100 mmol/L NaCl stress. In addition, three transcription factors, MYB, bHLH and NAC, were involved in the regulation of flavonoid accumulation under salt stress. The results of quantitative real-time PCR (qRT‒PCR) validation showed that the expression levels of 11 DEGs, including 7 genes involved in flavonoid biosynthesis, under different salt concentrations were consistent with the transcriptomic data, and parallel reaction monitoring (PRM) results showed that the expression levels of 6 key enzymes (proteins) involved in flavonoid synthesis were consistent with the accumulation of flavonoids. This study provides a new perspective for investigating the regulatory role of flavonoid biosynthesis in the regulation of mulberry seed germination under salt stress at different concentrations.

Double-Salts Super Concentrated Carbonate Electrolyte Boosting Electrochemical Performance of Ni-Rich LiNi0.90Co0.05Mn0.05O2 Lithium Metal Batteries.

Current lithium-ion batteries cannot meet the requirement of higher energy density with further large-scale application of electrical vehicles. Lithium metal batteries combined with Ni-rich layered oxides cathode are expected as the one of promising solutions, while the poor electrode and electrolyte interface impedes the commercial development of lithium metal batteries. A new double-salts super concentrated (DSSC) carbonate electrolyte is proposed to improve the electrochemical performance of LiNi0.90Co0.05Mn0.05O2 (NCM9055)||Li metal battery which exhibits stable cycling performance with the capacity retention of 93.04% and reversible capacity of 173.8 mAh g-1 after 100 cycles at 1 C, while cells with conventional 1 m diluted electrolyte remains only 60.55% and capacity of 114.2 mAh g-1. The double salts synergistic effect in super concentrated electrolyte promotes the formation for more balanced stable cathode electrolyte interface (CEI) inorganic compounds of CFx, LiNOx, SOF2, Li2SO4, and less LiF by X-ray photoelectron spectroscopy (XPS) test, and the uniform 2-3 nm rock-salt phase protection layer on the cathode surface by transmission electron microscope (TEM) characterization, improving the cycling performance of the Ni-rich NCM9055 layered oxide cathode. The DSSC electrolyte also can relief the Li dendrite growth on Li metal anode, as well as exhibit better flame retardance, promoting the application of more safety Ni-rich NCM9055||Li metal batteries.

Spherical Shell with CNTs Network Structuring Fe-Based Alluaudite Na2+2 δ Fe2- δ (SO4 )3 Cathode and Novel Phase Transition Mechanism for Sodium-Ion Battery.

Iron-based sulfate cathodes of alluaudite Na2+2 δ Fe2- δ (SO4 )3 (NFS) in sodium-ion batteries with low cost, steady cycling performance, and high voltage are promising for grid-scale energy storage systems. However, the poor electronic conductivity and the limited understanding of the phase-evolution of precursors hinder obtaining high-rate capacity and the pure phase. Distinctive NFS@C@n%CNTs (n = 1, 2, 5, 10) sphere-shell conductive networks composite cathode materials are constructed creatively, which exhibit superior reversible capacity and rate performance. In detail, the designed NFS@C@2%CNTs cathode delivers an initial discharge capacity of 95.9 mAh g-1 at 0.05 C and up to 60 mAh g-1 at a high rate of 10 C. The full NFS@C@2%CNTs//HC cell delivers a practical operating voltage of 3.5 V and mass-energy density of 140 Wh kg-1 at 0.1 C, and it can also retain 67.37 mAh g-1 with a capacity retention rate of 96.4% after 200 cycles at 2 C. On the other hand, a novel combination reaction mechanism is first revealed for forming NFS from the mixtures of Na2 Fe(SO4 )2 ·nH2 O (n = 2, 4) and FeSO4 ·H2 O during the sintering process. The inspiring results would provide a novel perspective to synthesize high-performance alluaudite sulfate and analogs by aqueous methods.

Robust Superhydrophobic Composite Fabric with Self-Healing and Chemical Durability.

Superhydrophobic fabrics with multiple functions have become a research hotspot. However, it is challenging to make self-healing mechanically robust and eco-friendly superhydrophobic fabrics, which are limited by complex fabrication processes and excessive use of environmentally unfriendly solvents during fabrication. Herein, inspired by the secretion of a waxy substance from the surface of lotus leaves to restore water repellency, self-healing superhydrophobic composite fabrics (as-synthesized PA66/6-PET@Tico) are obtained by constructing a papillary TiO2 and tentacle-like fluorinated acrylate polymer (FCB015) coating on polyester-nylon composite fabrics using two-step hydrothermal method. The result indicates that PA66/6-PET@Tico with hierarchical micro/nanostructure exhibits excellent superhydrophobic and self-healing properties. Compared with FCB015 coated fabric, the contact angles (CA) of water and soybean oil rise to 172.2° and 166.8° from 137.4° and 98.8°, respectively. After mechanical abrasion, PA66/6-PET@Tico recovers a water contact angle (WCA) of 165.6° at room temperature. The WCA remains higher than 155° after 18 h of chemical corrosion. Furthermore, the bacterial inhibition rates of PA66/6-PET@Tico for Staphylococcus Aureus and Escherichia Coli are 99.90 and 98.38%, respectively. In this work, a new idea is proposed for designing a simple and effective self-healing superhydrophobic coating, expecting to promote the large-scale industrial production and application of functional surfaces.

Multiple strategies, including 6mA methylation, affecting plant alternative splicing in allopolyploid peanut.

Alternative splicing (AS), an important post-transcriptional regulation mechanism in eukaryotes, can significantly increase transcript diversity and contribute to gene expression regulation and many other complicated developmental processes. While plant gene AS events are well described, few studies have investigated the comprehensive regulation machinery of plant AS. Here, we use multi-omics to analyse peanut AS events. Using long-read isoform sequencing, 146 464 full-length non-chimeric transcripts were obtained, resulting in annotation corrections for 1782 genes and the identification of 4653 new loci. Using Iso-Seq RNA sequences, 271 776 unique splice junctions were identified, 82.49% of which were supported by transcriptome data. We characterized 50 977 polyadenylation sites for 23 262 genes, 12 369 of which had alternative polyadenylation sites. AS allows differential regulation of the same gene by miRNAs at the isoform level coupled with polyadenylation. In addition, we identified many long non-coding RNAs and fusion transcripts. There is a suppressed effect of 6mA on AS and gene expression. By analysis of chromatin structures, the genes located in the boundaries of topologically associated domains, proximal chromosomal telomere regions, inter- or intra-chromosomal loops were found to have more unique splice isoforms, higher expression, lower 6mA and more transposable elements (TEs) in their gene bodies than the other genes, indicating that chromatin interaction, 6mA and TEs play important roles in AS and gene expression. These results greatly refine the peanut genome annotation and contribute to the study of gene expression and regulation in peanuts. This work also showed AS is associated with multiple strategies for gene regulation.

Metasurface-Assisted Quantum Nonlocal Weak-Measurement Microscopy.

In standard quantum weak measurements, preselection and postselection of quantum states are implemented in the same photon. Here we go beyond this restrictive setting and demonstrate that the preselection and postselection can be performed in two different photons, if the two photons are polarization entangled. The Pancharatnam-Berry phase metasurface is incorporated in the weak measurement system to perform weak coupling between probe wave function and spin observable. By introducing nonlocal weak measurement into the microscopy imaging system, it allows us to remotely switch different microscopy imaging modes of pure-phase objects, including bright-field, differential, and phase reconstruction. Furthermore, we demonstrate that the nonlocal weak-measurement scheme can prevent almost all environmental noise photons from detection and thus achieves a higher image contrast than the standard scheme at a low photon level. Our results provide the possibility to develop a quantum nonlocal weak-measurement microscope for label-free imaging of transparent biological samples.

Designing future peanut: the power of genomics-assisted breeding.

KEY MESSAGE: Integrating GAB methods with high-throughput phenotyping, genome editing, and speed breeding hold great potential in designing future smart peanut cultivars to meet market and food supply demands. Cultivated peanut (Arachis hypogaea L.), a legume crop greatly valued for its nourishing food, cooking oil, and fodder, is extensively grown worldwide. Despite decades of classical breeding efforts, the actual on-farm yield of peanut remains below its potential productivity due to the complicated interplay of genotype, environment, and management factors, as well as their intricate interactions. Integrating modern genomics tools into crop breeding is necessary to fast-track breeding efficiency and rapid progress. When combined with speed breeding methods, this integration can substantially accelerate the breeding process, leading to faster access of improved varieties to farmers. Availability of high-quality reference genomes for wild diploid progenitors and cultivated peanuts has accelerated the process of gene/quantitative locus discovery, developing markers and genotyping assays as well as a few molecular breeding products with improved resistance and oil quality. The use of new breeding tools, e.g., genomic selection, haplotype-based breeding, speed breeding, high-throughput phenotyping, and genome editing, is probable to boost genetic gains in peanut. Moreover, renewed attention to efficient selection and exploitation of targeted genetic resources is also needed to design high-quality and high-yielding peanut cultivars with main adaptation attributes. In this context, the combination of genomics-assisted breeding (GAB), genome editing, and speed breeding hold great potential in designing future improved peanut cultivars to meet market and food supply demands.

Cultivated alien plants with high invasion potential are more likely to be traded online in China.

Biological invasions have become a worldwide problem, and measures to efficiently prevent and control invasions are still in development. Like many other parts of the world, China is undergoing a dramatic increase in plant invasions. Most of the currently 933 established (i.e., naturalized) plant species, of which 214 are categorized as invasive, have been introduced into China for cultivation. It is likely that many of those species are still being traded, particularly online, by plant nurseries. However, studies assessing whether naturalized and invasive species are currently being traded more or less than nonnaturalized aliens are rare. We extracted online-trade information for 13,718 cultivated alien plant taxa on 1688.com, the largest website for domestic B2B in China. We analyzed how the presence in online-nursery catalogs, the number of online nurseries that offerred the species for sale, and the product type (i.e., seeds, live plants and vegetative organs) differed among nonnaturalized, naturalized noninvasive, and invasive species. Compared to nonnaturalized taxa, naturalized noninvasive and invasive taxa were 3.7-5.2 times more likely to be available for purchase. Naturalized noninvasive and invasive taxa were more frequently offered as seeds by online nurseries, whereas nonnaturalized taxa were more frequently offered as live plants. Based on these findings, we propose that, to reduce the further spread of invasive and potentially invasive plants, implementation of plant-trade regulations and a monitoring system of the online horticultural supply chain will be essential.

Membraneless Electrochemical Synthesis Strategy toward Nitrate-to-Ammonia Conversion.

Electroreduction of nitrate (NO3RR) to ammonia in membraneless electrolyzers is of great significance for reducing the cost and saving energy consumption. However, severe chemical crossover with side reactions makes it challenging to achieve ideal electrolysis. Herein, we propose a general strategy for efficient membraneless ammonia synthesis by screening NO3RR catalysts with inferior oxygen reduction activity and matching the counter electrode (CE) with good oxygen evolution activity while blocking anodic ammonia oxidation. Consequently, screening the available Co-Co system, the membraneless NO3--to-NH3 conversion performance was significantly higher than H-type cells using costly proton-exchange membranes. At 200 mA cm-2, the full-cell voltage of the membraneless system (∼2.5 V) is 4 V lower than that of the membrane system (∼6.5 V), and the savings are 61.4 kW h (or 56.9%) per 1 kg NH3 produced. A well-designed pulse process, inducing reversible surface reconstruction that in situ generates and restores the active Co(III) species at the working electrode and forms favorable Co3O4/CoOOH at the CE, further significantly improves NO3--to-NH3 conversion and blocks side reactions. A maximum NH3 yield rate of 1500.9 µmol cm-2 h-1 was achieved at -0.9 V (Faraday efficiency 92.6%). This pulse-coupled membraneless strategy provides new insights into design complex electrochemical synthesis.

Predictive value of the serum uric acid to high-density lipoprotein cholesterol ratio for culprit plaques in patients with acute coronary syndrome.

BACKGROUND: Hyperuricemia and low level of high-density lipoprotein cholesterol (HDL-C) are both risk factors for coronary artery disease (CAD). The uric acid to HDL-C ratio (UHR) has recently been identified as a new inflammatory and metabolic biomarker. However, the relationship between the UHR and coronary culprit plaques has not been fully investigated in patients with acute coronary syndrome (ACS). METHODS: A total of 346 patients with ACS were enrolled in this study. Culprit lesion characteristics were assessed by optical coherence tomography (OCT). Logistic regression and linear correlation analyses were performed to assess the association between the UHR and culprit plaques. The predictive value of the UHR was investigated by receiver operating characteristic (ROC) curve analysis. RESULTS: The percentages of typical culprit plaques, including ruptures, erosions and thrombi, were greater in the high-UHR subgroup than those in the low-UHR subgroup. A positive relationship was also found between the UHR and diameter stenosis (r = 0.160, P = 0.003) and between the UHR and area stenosis (r = 0.145, P = 0.007). The UHR was found to be independently associated with plaque rupture, erosion and thrombus. Furthermore, ROC analysis suggested that the UHR had a better predictive value than low-density lipoprotein cholesterol. CONCLUSIONS: An elevated UHR level was independently related to the occurrence rate of culprit plaques. The UHR is a simple and easily acquired parameter for detecting culprit plaques in patients with ACS.

Decoding active compounds and molecular targets of herbal medicine by high-throughput metabolomics technology: A systematic review.

Clinical experiences of herbal medicine (HM) have been used to treat a variety of human intractable diseases. As the treatment of diseases using HM is characterized by multi-components and multi-targets, it is difficult to determine the bio-active components, explore the molecular targets and reveal the mechanisms of action. Metabolomics is frequently used to characterize the effect of external disturbances on organisms because of its unique advantages on detecting changes in endogenous small-molecule metabolites. Its systematicity and integrity are consistent with the effective characteristics of HM. After HM intervention, metabolomics can accurately capture and describe the behavior of endogenous metabolites under the disturbance of functional compounds, which will be used to decode the bioactive ingredients of HM and expound the molecular targets. Metabolomics can provide an approach for explaining HM, addressing unclear clinical efficacy and undefined mechanisms of action. In this review, the metabolomics strategy and its applications in HM are systematically introduced, which offers valuable insights for metabolomics methods to characterizing the pharmacological effects and molecular targets of HM.

Nanobiotechnology augmented cancer stem cell guided management of cancer: liquid-biopsy, imaging, and treatment.

Cancer stem cells (CSCs) represent both a key driving force and therapeutic target of tumoral carcinogenesis, tumor evolution, progression, and recurrence. CSC-guided tumor diagnosis, treatment, and surveillance are strategically significant in improving cancer patients' overall survival. Due to the heterogeneity and plasticity of CSCs, high sensitivity, specificity, and outstanding targeting are demanded for CSC detection and targeting. Nanobiotechnologies, including biosensors, nano-probes, contrast enhancers, and drug delivery systems, share identical features required. Implementing these techniques may facilitate the overall performance of CSC detection and targeting. In this review, we focus on some of the most recent advances in how nanobiotechnologies leverage the characteristics of CSC to optimize cancer diagnosis and treatment in liquid biopsy, clinical imaging, and CSC-guided nano-treatment. Specifically, how nanobiotechnologies leverage the attributes of CSC to maximize the detection of circulating tumor DNA, circulating tumor cells, and exosomes, to improve positron emission computed tomography and magnetic resonance imaging, and to enhance the therapeutic effects of cytotoxic therapy, photodynamic therapy, immunotherapy therapy, and radioimmunotherapy are reviewed.

Developing defined starter culture for reproducible profile of flavour compound in Chinese xiaoqu baijiu fermentation.

Defined starter cultures, containing selected microbes could reduce the complexity of natural starter, are beneficial for controllable food fermentations. However, there are challenges in identifying key microbiota and constructing synthetic microbiota for traditional food fermentations. Here, we aimed to develop a defined starter culture for reproducible profile of flavour compounds, using Chinese Xiaoqu Baijiu fermentation as a case. We classified all microbes into 4 modules using weighted correlation network analysis. Module 3 presented significant correlations with flavour compounds (P < 0.05) and the highest gene abundance related with flavour compound production. 13 dominant species in module 3 were selected for mixed culture fermentation, and each species was individually deleted to analyse the effect on flavour compound production. Ten species, presenting significant effects (P < 0.05) on flavour compound production, were selected for developing the starter culture, including Rhizopus oryzae, Rhizopus microsporus, Saccharomyces cerevisiae, Pichia kudriavzevii, Wickerhamomyces anomalus, Lactobacillus acetotolerans, Levilactobacillus brevis, Weissella paramesenteroides, Pediococcus acidilactici, and Leuconostoc pseudomesenteroides. After optimising the structure of the starter culture, the profile similarity of flavour compounds produced by the starter culture reached 81.88% with that by the natural starter. This work indicated feasibility of reproducible profile of flavour compounds with defined starter culture for food fermentations.

Dimensions of invasiveness: Links between local abundance, geographic range size, and habitat breadth in Europe's alien and native floras.

Understanding drivers of success for alien species can inform on potential future invasions. Recent conceptual advances highlight that species may achieve invasiveness via performance along at least three distinct dimensions: 1) local abundance, 2) geographic range size, and 3) habitat breadth in naturalized distributions. Associations among these dimensions and the factors that determine success in each have yet to be assessed at large geographic scales. Here, we combine data from over one million vegetation plots covering the extent of Europe and its habitat diversity with databases on species' distributions, traits, and historical origins to provide a comprehensive assessment of invasiveness dimensions for the European alien seed plant flora. Invasiveness dimensions are linked in alien distributions, leading to a continuum from overall poor invaders to super invaders-abundant, widespread aliens that invade diverse habitats. This pattern echoes relationships among analogous dimensions measured for native European species. Success along invasiveness dimensions was associated with details of alien species' introduction histories: earlier introduction dates were positively associated with all three dimensions, and consistent with theory-based expectations, species originating from other continents, particularly acquisitive growth strategists, were among the most successful invaders in Europe. Despite general correlations among invasiveness dimensions, we identified habitats and traits associated with atypical patterns of success in only one or two dimensions-for example, the role of disturbed habitats in facilitating widespread specialists. We conclude that considering invasiveness within a multidimensional framework can provide insights into invasion processes while also informing general understanding of the dynamics of species distributions.

Association between body mass index and lymph node metastasis among women with cervical cancer: a systematic review and network meta-analysis.

PURPOSE: Lymph node status is a determinant of survival in patients with early-stage cervical cancer. However, the relationship between obesity and lymph node status remains unclear. Therefore, this systematic review aims to evaluate the correlation between body mass index (BMI) and lymph node metastasis in cervical cancer. METHODS: Cohort studies through six databases were reviewed until December 2021. Odds ratios (ORs) for lymphatic metastasis were estimated using random-effects models and network meta-analysis. BMI groups for lymph node metastasis were ranked. Heterogeneities were assessed using I2. Subgroup analyses were performed to determine possible sources of heterogeneity. RESULTS: No significant difference was found between obese (BMI ≥ 25) and non-obese patients (BMI < 25) (OR = 1.01; 95% CI 0.69-1.47; P = 0.97). In subgroup analyses, obesity was associated with higher risk among the Americans and advanced-stage patients. The grouping analysis based on BMI and the rankogram values revealed that the '35 ≤ BMI' group had the highest risk of lymph node metastasis. CONCLUSION: Although there were no significant differences in lymph node metastasis between obese and non-obese cervical cancer patients in overall analysis, patients with BMI ≥ 35 were at significantly higher risk of lymph node metastasis.