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PubMed2026

Integrated widely targeted metabolomics and GC-IMS reveal dynamic flavor, nutritional, functional, and metabolic profiles in macadamia kernels during processing.

Different processing stages influence the color, flavor, and antioxidant activities of macadamia kernels. However, the biochemical mechanisms that occur during processing are not well known. This study integrated widely targeted metabolomics (UPLC-MS/MS) with GC-IMS to systematically characterize non-volatile and volatile compounds in macadamia kernels across key three sample groups: fresh kernels (FMN), low-temperature-dried kernels (DMN), and roasted kernels (BMN). A total of 622 non-volatile metabolites and 52 volatile compounds were identified. Low-temperature drying promoted the accumulation of phenolic acids and flavonoids, enhancing antioxidant capacity. Roasting degraded heat-sensitive nutrients but generated flavor compounds via Maillard reaction and lipid oxidation, shifting aroma from green to nutty notes. Nutritional assessment confirmed that roasting significantly reduced antioxidant activities and bile acid binding capacity. Pearson correlation analysis verified the key metabolite-antioxidant relationships. These findings provide critical insights into metabolic dynamics during nut processing and establish a scientific basis for optimizing thermal processing strategies.

Metabolomics↗

PubMed2026

Enhancement flavor quality in Zhao'an Baxian oolong tea through enhanced turning-over process.

A systematical investigation on the effects of turning-over intensity on the flavor formation of Zhao'an Baxian oolong tea (ZBT) was performed, through a comparative analysis of heavy turning-over (HT) and light turning-over (LT) treatments in this study. The tea samples were subjected to proteomic and metabolomic analyses, combined with quantitative descriptive analysis (QDA) and electronic sensory (E-tongue/E-nose) evaluation. The results demonstrate that HT significantly reduced the content of bitter and astringent compounds, such as catechins and flavonol glycosides, while promoting the accumulation of umami-related amino acids. Concurrently, HT enhanced the biosynthesis of key floral and fruity volatiles, such as β-ocimene, geraniol, benzaldehyde, jasmone by activating stress-responsive metabolic pathways. These coordinated biochemical changes, driven by enzyme-catalyzed reactions in response to prolonged mechanical wounding and environmental stress, collectively improved the overall sensory profile of ZBT. These findings provide a mechanistic foundation for improving ZBT production, with clear implications for quality control and flavor-oriented product development.

Tea↗

PubMed2026

Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.

Genomic prediction of multiple phenotypes is crucial in modern plant breeding; however, existing methods struggle with negative transfer and lack interpretability, particularly across high-dimensional small-sample data and diverse species. To address this, we propose Mul-PheG2P, a novel paradigm based on decoupled learning and predictive space fusion. It employs a two-stage design: first training phenotype-specific encoders using genetic data, then decoupling phenotype-specific learning from cross-phenotype aggregation via an interpretable prediction layer. Mul-PheG2P outperforms existing methods across diverse crop datasets, including maize (Zea mays), wheat (Triticum aestivum), and tomato (Solanum lycopersicum). It provides a multi-scale interpretability chain: at the macro level, it quantifies phenotypic contributions via attention-based weighting; at the micro level, Integrated Gradients reveal the genetic basis of predictions. Notably, the model successfully identified the CCT (CONSTANS, CO-like, and TOC) motif regulating photoperiodism and the SQUAMOSA (SQUAMOSA promoter binding protein) promoter for inflorescence development, confirming its ability to capture functional biological mechanisms. These results highlight the high performance and interpretability of Mul-PheG2P, showcasing its value for low-cost, large-scale screening to advance precision breeding.

Phenotype↗

PubMed2026

Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.

PURPOSE: To synthesizes evidence on artificial intelligence (AI) performance in neuroblastoma (NB) diagnosis, risk stratification, prognosis, and genomic characterization. MATERIALS AND METHODS: A systematic review and meta-analysis was conducted following PRISMA 2020 guidelines (PROSPERO: CRD42024539475) across five databases. Meta-analyses used random-effects models with logit-transformed Area Under the Curve (AUCs) and cluster-robust standard errors. AI models were classified as Machine Learning Models (MLM) or Hybrid Nomograms (HN) based on their construction methodology. RESULTS: Of 3,742 articles identified, 53 were included. MLMs demonstrated higher point estimates than radiologists in differential diagnosis (AUC: 0.87 vs. 0.83), though this difference was not statistically significant and carried substantial uncertainty. HNs achieved stronger performance in risk stratification (AUC: 0.87). AI-derived nomograms (AUC: 0.9) and gene signatures (AUC: 0.8) outperformed conventional prognostic markers descriptively. Chemotherapy response prediction remained below clinical utility thresholds across all model types. Only 33.9% of models reported calibration and 24.5% underwent external validation. CONCLUSIONS: AI demonstrates proof-of-concept across multiple NB clinical domains. However, clinical adoption remains premature given persistent gaps in external validation, calibration, dataset size, and pediatric-specific model development. Future studies should test these models prospectively in multicenter pediatric cohorts, ideally through COG or SIOPEN, using shared definitions for diagnosis, risk group, treatment response, and survival outcomes.

Humans↗

PubMed2026

Prevalence and Factors Associated with Receiving a Prescription for a Direct Oral Anticoagulant Among Patients with Atrial Fibrillation on Hospice Admission.

Atrial fibrillation (AF) is prevalent in hospice care, but anticoagulation decisions in this population are not well understood. In this cross-sectional study, we described the prevalence and characteristics associated with direct oral anticoagulant (DOAC) prescription on hospice admission. We used electronic health data from adult decedents with AF in a large, for-profit hospice chain in the United States between January 1, 2017 and December 31, 2019. We used multivariable logistic regression with results reported as adjusted odds ratios (AORs) and 95% confidence intervals (CIs). Among 13,233 decedents, mean (standard deviation [SD]) age was 84.2 (9.9) years, 53.6% were female, 65.1% were White, and 56.1% were referred to hospice from a hospital. Mean (SD) CHA2DS2-VASc score were 3.8 (1.4) for males and 4.8 (1.3) for females, and mean (SD) HAS-BLED score was 2.2 (1.0). Overall, 8% of patients received a DOAC prescription on hospice admission. Characteristics associated with receiving a DOAC prescription included PPS scores of ≥ 20% (compared to scores < 20%), and receiving hospice care at home, nursing home, assisted living facility, or residential care home (compared to inpatient hospice). Further studies about the risks and benefits of DOAC use are needed to optimize decision-making in this population.

DOAC↗

PubMed2026

Engineered MXene-based nanozyme platform: NIR-II photothermal and dual enzyme-mimetic potentiated chemodynamic synergy for precision tumor eradication.

The antioxidant defense barrier in the tumor microenvironment, particularly glutathione (GSH), considerably restricts the therapeutic efficacy of chemodynamic therapy (CDT). Moreover, CDT generally exhibits relatively mild therapeutic efficacy owing to its intrinsic reaction kinetics, making it difficult to achieve complete tumor eradication within a short time. To address these issues, we construct a functionalized nanotherapeutic platform, Nb2CTx@Ru-PEG2000-FA (NCRPF), for tumor photothermal ablation and enhanced CDT resulting from GSH depletion. NCRPF possesses three key advantages: 1. Efficient near-infrared II photothermal conversion (η = 42.08%), raising the tumor temperature above 45 °C within 90 s for rapid ablation; 2. Dual peroxidase-like and glutathione peroxidase-like activities, simultaneously depleting GSH and generating a burst of ·OH to eliminate residual tumors; 3. Targeted tumor accumulation with 2.9-fold higher efficiency than passive diffusion. Both in vitro and in vivo results confirm that this combined strategy achieves complete tumor eradication with favorable biosafety. Collectively, the NCRPF nanotherapeutic system provides a powerful new paradigm with high translational potential for the complete eradication of breast cancer.

Animals↗

PubMed2026

Genome-wide SNP data support species boundaries in sympatric Polylepis Ruiz & Pav. (Rosaceae) species from Bolivia and Ecuador.

Species delimitation in the South American genus Polylepis is notoriously challenging due to high morphological similarity and phenotypic plasticity, likely driven by hybridization and gene flow. Previous phylogenetic studies suggested that genetic structure aligns more strongly with geography than with taxonomy, questioning existing species concepts and hampering conservation efforts. We used double-digest RAD sequencing (ddRADseq) to generate genome-wide SNP data for 11 Polylepis species sampled across multiple localities in Bolivia and Ecuador. Population genetic analyses, phylogenetic inference, and network approaches were combined to assess whether genetic structure aligns more closely with taxonomy or geography. Morphologically defined species formed largely cohesive genetic lineages across regions, with species identity explaining substantially more genetic variation than locality. While localized admixture and reticulation were detected among closely related taxa, widespread species showed strong genetic cohesion and clear separation from congeners. Our results indicate that the sampled Polylepis species from Bolivia and Ecuador maintain distinct genetic identities despite localized signals consistent with gene flow. This genome-wide support for current taxonomy highlights Polylepis as a valuable model for studying speciation under gene flow and indicates that multiple geographic sampling will be essential in reconstructing a robust phylogeny of the genus, with important implications for conservation planning in Andean montane forests.

Bolivia↗

PubMed2026

LINE-1 repeats are a defining feature of the Xce.

During early development, female mammals inactivate one X chromosome to balance their X-linked gene dosage with males. While allelic choice is random in inbred mouse populations, choice can be significantly skewed in interstrain hybrids. The genetic basis of skewing has long been attributed to the mysterious "X chromosome controlling element(s)" (Xce) with different strengths among species, subspecies, and strains. When two X-chromosomes with different Xce strengths are inherited by offspring, the X chromosome with the stronger Xce will have a higher probability of remaining active. Here, we provide evidence that L1Tf repeats-a subfamily of long interspersed nuclear elements 1-plays a role in determining Xce strength. L1Tf elements form a condensed core within the inactive X (Xi) territory. Mouse strains with varying Xce strengths differ in the L1Tf copy number on the X chromosome, with the strength of the Xce allele being inversely related to L1Tf copy number. L1Tf expression mediates the Xce effect. However, in contrast to a prior report, L1Tf RNA does not coat the Xi. Rather, L1Tf promotes condensation of the Xi core. Intriguingly, L1Tfs recruit and sequester YY1 from active genes, accelerating XCI in cis. Thus, L1Tf copy number, expression, and binding of YY1 are key defining features of the Xce. We propose a model in which the Xce influences the choice of Xist alleles by promoting YY1 binding to the nucleation site for the initiation of Xist spreading.

Animals↗

PubMed2026

Identification and functional analysis of MeJA-responsive bHLH family genes in Taraxacum kok-saghyz.

Taraxacum kok-saghyz (T. kok-saghyz) is considered a highly promising alternative source of natural rubber (NR), as its roots synthesize high-molecular-weight NR comparable to that produced by Hevea brasiliensis. The basic helix-loop-helix (bHLH) family of transcription factors (TFs) plays crucial roles in plant organogenesis, hormonal signal transduction, and the regulation of secondary metabolism. This study aimed to systematically identify TkbHLH family members and to elucidate their potential functions in responding to methyl jasmonate (MeJA) and regulating root development. Based on the T. kok-saghyz genome, 172 TkbHLH members were identified and phylogenetically classified into 16 subfamilies. Among these, 37 genes were selected due to their significant induction by MeJA. Sequence analysis confirmed all encoded proteins contain the conserved bHLH domain. Subcellular localization verified nuclear localization of five core TkbHLH proteins. Interactions were shown by yeast two-hybrid and bimolecular fluorescence complementation, revealing these proteins form homodimers and heterodimers. Notably, a specific interaction was detected between TkbHLH162 and TkHMGS1, a key enzyme in the mevalonate (MVA) pathway, suggesting a potential molecular link between JA signaling and the rubber biosynthesis precursor pathway. Functional characterization via overexpression assays showed that selected TkbHLH genes significantly either promoted or inhibited root elongation. In summary, this study presents the first systematic characterization of the bHLH TF family in T. kok-saghyz, elucidating its involvement in JA signal response, protein interaction networks, and root development regulation. These findings provide a crucial foundation for further investigation into the molecular mechanisms by which TkbHLH TFs influence root morphogenesis and NR biosynthesis in T. kok-saghyz.

Taraxacum kok-saghyz (T. kok-saghyz)↗

PubMed2026

Are there any common effects in preclinical models of micro- and nanoplastic (MNP) exposure? A systematic review.

Micro- and nanoplastics (MNPs) are emerging contaminants detected in food sources and the marine food chain, raising concerns about human health. Although no causal relationship has been established between MNP exposure and specific diseases, growing evidence suggests adverse developmental, behavioral, cognitive and biochemical effects. This systematic review synthesized evidence from common preclinical neurotoxicology models, including C. elegans, D. rerio, D. melanogaster, in vitro systems and rodents, to identify convergent developmental, behavioral and biochemical outcomes. The protocol was preregistered in OSF, followed PRISMA-P guidelines, applied PICOS criteria, and assessed methodological quality using the European Commission's ToxRTool. Overall, 185 studies were included. Consistent findings showed impaired survival and disrupted development across all models. Behavioral alterations affecting anxiety, memory, learning, sociability and locomotor activity were also consistently reported. In addition, numerous studies identified disruptions in the serotonergic (5-HT) system, including changes in neurotransmitter levels, transporters and metabolic enzymes. Despite methodological heterogeneity, these findings indicate that MNP exposure produces reproducible neurodevelopmental and neurochemical alterations across experimental models. Future studies should improve methodological harmonization, strengthen cross-model comparability and identify robust biomarkers and key mechanisms underlying MNP-induced neurotoxicity, facilitating translation to human health risk assessment frameworks.

Animals↗

PubMed2026

Cytonuclear conflict and reticulate evolution in the Morelloid clade (Solanum, Solanaceae): Insights from genome skimming and network Phylogenomics.

The Morelloid clade (black nightshades) is one of the most strongly supported clades within the megadiverse Solanum genus. It comprises 76 globally distributed, non-spiny herbaceous and suffrutescent species. While often erroneously considered poisonous weeds, several species are economically important as orphan crops. The clade is closely related to tomato and potato but, due to a lack of focused breeding efforts, remains a putative reservoir of genetic diversity for crop improvement. Despite this potential, we lack fundamental knowledge on the evolution of the Morelloid clade. The group includes polyploid species with unknown parental origins-likely reflecting reticulate processes such as hybridization, introgression, and associated backcrossing events. Prior analyses have been unable to disentangle these processes, leaving the mechanisms underlying reticulate evolution in the Morelloid clade poorly understood. Here, we use genome skimming to produce a well-supported maximum likelihood plastid phylogeny from complete circularized plastomes and a coalescent-based species tree from combined Angiosperms353 and conserved ortholog set nuclear markers. Our dataset, composed of previously published data and deep genome skimming from herbarium samples, spans 26 Morelloid species. To investigate phylogenetic discordance, we used a nuclear phylogenetic network, multispecies coalescent simulations, a fused rooted nuclear chloroplast tree, and quantification of nuclear gene tree concordance. We show that incongruence between nuclear and plastid trees is pervasive and cannot be explained by incomplete lineage sorting alone. Instead, our results demonstrate that events consistent with repeated chloroplast capture have shaped the reticulate evolutionary history of the clade, especially among African polyploid and Pan-American diploid lineages.

Phylogeny↗

PubMed2026

Mechanistic Insights Into the Association Between Gut Microbiota Diversity and Atherosclerosis, Acute Coronary Syndrome, and Peripheral Arterial Disease Progression.

BACKGROUND: The gut microbiome has emerged as a potential contributor to cardiovascular diseases (CVDs), including atherosclerosis, acute coronary syndrome (ACS), and peripheral arterial disease (PAD). While observational studies link dysbiosis to CVD, causal relationships remain uncertain. METHODS: This narrative review synthesizes evidence from human observational studies, clinical interventions, and experimental models to distinguish association from mechanistic plausibility and clinical causality. Literature was searched through July 2026 in PubMed/MEDLINE, Web of Science, and Scopus. RESULTS: Microbial metabolites-including trimethylamine N-oxide (TMAO), short-chain fatty acids (SCFAs), bile acids, and lipopolysaccharide (LPS)-modulate endothelial function, immune cell programming, platelet activity, and plaque stability through receptor-mediated signaling and epigenetic regulation. SCFAs demonstrate potentially protective effects via GPCR and HDAC pathways, while TMAO is associated with atherothrombotic risk. However, much mechanistic evidence derives from preclinical studies. Heterogeneity from diet, geography, host characteristics, renal function, and medications substantially influences microbiota-CVD associations. CONCLUSION: The gut-vascular connection is biologically plausible, but definitive clinical causality remains unproven. Microbiome-directed therapies (dietary modulation, pre/pro/synbiotics, targeted metabolite inhibition) are investigational. Prospective, standardized, adequately powered human studies with clinically meaningful outcomes are essential before routine cardiovascular application.

Gastrointestinal Microbiome↗

PubMed2026

Emergence of an optrA-positive Enterococcus faecalis ST699 lineage in animal-derived foods in Beijing, China.

Enterococci from animal-derived foods are key reservoirs for antimicrobial resistance (AMR) in the food chain. However, comparative genomic studies investigating the distribution of the oxazolidinone resistance gene optrA among food- and human-derived Enterococci remain limited. This study assessed linezolid-resistant Enterococci from retail meat and healthy humans in Beijing, China (2023-2024). Among 87 isolates, E. faecalis and E. faecium predominated. Food-derived isolates showed broader resistance profiles than human isolates. Fourteen optrA-positive strains were identified, accounting for 92.9% of food isolates. optrA frequently co-localized with erm(A), ant(9)-Ia, and fexA on Tn554-family transposons, suggesting a potentially transferable multidrug resistance module. Notably, an optrA-positive E. faecalis ST699 clone was identified for the first time in Chinese retail meat. This clone formed a distinct lineage and carried a complete Tn554-optrA island. A representative ST699 isolate exhibited enhanced fitness and virulence potential in the Galleria mellonella model. These findings highlight animal-derived foods as important reservoirs of linezolid-resistant Enterococci and provide genomic evidence consistent with their role as potential sources of optrA-mediated resistance. The emergence of a multidrug-resistant E. faecalis ST699 clone with enhanced fitness characteristics underscores the need for continued surveillance of foodborne antimicrobial resistance within the One Health framework.

Enterococcus faecalis↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2026

Metagenome-scale modeling to assess microbiome metabolic complementarity for precision microbiota transplantation therapies.

Fecal microbiota transplantation (FMT) holds therapeutic promise beyond recurrent Clostridioides difficile infection, but clinical outcomes remain unpredictable and donor-selection strategies remain limited, in part because the role of donor‒recipient metabolic interactions in shaping the post-FMT community remains poorly understood. Here, we leverage metagenome-scale metabolic modeling to quantify metabolic niche complementarity between donor and recipient microbiomes and predict post-FMT community composition. Using MICOM-derived metabolic models, we show that donor genomes whose metabolic flux profiles are more dissimilar from the recipient community colonize at significantly higher rates in a murine FMT model. In a human IBS trial, the same metric predicted post-FMT community composition via leave-one-out cross-validation and captured known disease-associated alterations in short-chain fatty acid, sulfur, and gas metabolism. We then performed 2,548 in silico FMT simulations between IBS-D/M patients and donors from the OpenBiome biobank to evaluate personalized donor screening, identifying super-donors characterized by high taxonomic diversity, broad metabolic niche coverage, and community interaction networks dominated by cross-feeding rather than competition. Together, these results support metabolic niche complementarity as a potential determinant of post-FMT community composition and provide a mechanistic basis for evaluating donor-recipient metabolic compatibility. This framework offers a scalable approach for generating testable hypotheses for personalized donor selection.

Fecal Microbiota Transplantation↗

PubMed2026

A complete hlyCABD-like RTX operon marks a virulence-associated subset of trh-positive Vibrio parahaemolyticus from Hangzhou Bay, China.

Vibrio parahaemolyticus remains a major cause of seafood-associated gastroenteritis, yet routine surveillance still relies largely on the canonical hemolysin markers thermostable direct hemolysin (tdh) and tdh-related hemolysin (trh). To determine whether this framework overlooks accessory virulence determinants in trh-positive lineages, we analyzed 193 V. parahaemolyticus isolates collected between 2022 and 2025 from clinical, environmental, and seafood-associated sources in the Hangzhou Bay region of China. Serotyping identified 45 serotypes, with O10:K4 predominating among clinical isolates. Both clinical and non-clinical populations showed open pan-genomes, although the non-clinical group carried a larger accessory gene pool. We identified a complete hlyCABD-like RTX operon in 10 trh-positive isolates with T3SS2-associated virulence backgrounds. These RTX-positive isolates were distributed across seven sequence types and three of five phylogenetic groups. This distribution was lineage-restricted but non-clonal. In the representative hybrid-assembled genome, the operon occurred within a mosaic genomic region containing additional virulence- and mobility-associated genes, indicating a composite pathogenicity island-like element. In the tested subset, RTX-positive isolates showed significantly greater hemolytic activity than RTX-negative trh-positive isolates. This significant difference was consistently observed in both plate-based and liquid assays, and within the RTX-positive subset, hlyA expression correlated with hemolytic activity, whereas the trh gene and the tlh (thermolabile hemolysin) gene did not. A complete hlyCABD-like RTX operon therefore identifies a hemolysis-associated subset of trh-positive V. parahaemolyticus and supports its further evaluation as an additional target for food safety surveillance.

Vibrio parahaemolyticus↗

PubMed2026

Genomic and Molecular Interaction Analysis of NodD1 in a Novel Bradyrhizobium yuanmingense sp. B64 Isolate for Nodulation and Symbiosis of Legume Plants.

Rhizobial bacteria are known for their ability to fix nitrogen for leguminous plants and their essential function for sustainable agriculture. This study characterizes the taxonomic status and functional potential of the Bradyrhizobium B64 isolate using integrated genomic and molecular approaches. The whole genome of the B64 isolate was sequenced via Illumina paired-end technology. Species delimitation was performed using average nucleotide identity (ANI) and digital DNA-DNA Hybridization (dDDH). The NodD1 protein structure was modeled using AlphaFold3 and validated by Ramachandran plot analysis. Molecular docking was then conducted to evaluate interactions between NodD1 and four signaling flavonoids: Apigenin, Daidzein, Genistein, and Naringenin. Genomic analysis revealed a maximum ANI of 94.4% and dDDH values between 51.4 and 62.4%. Since these values fall below the standard prokaryotic thresholds (ANI < 95%; dDDH < 70%), the B64 isolate is identified as a novel species. Physiological assays confirmed nitrogen fixation (1.97 ppm), IAA production (3.67 ppm), and phosphate solubilization (26.10 ppm). Structural validation showed 100% of NodD1 residues in allowed regions, ensuring high model reliability. Docking simulations demonstrated strong binding affinities across all flavonoids, with binding free energies ranging from - 8.8 to - 9.0 kcal/mol. Daidzein exhibited the highest thermodynamic stability (- 9.0 kcal/mol), whereas apigenin showed the most extensive residue interaction network. The B64 isolate is a novel Bradyrhizobium species with a high symbiotic capacity. The stable NodD1-flavonoid interactions provide a molecular basis for efficient nodulation, positioning B64 as a promising candidate for developing lipo-chitooligosaccharide (LCO)-based biofertilizers.

Bradyrhizobium↗

PubMed2026

PaNDA: Efficient Optimization of Phylogenetic Diversity in Networks.

Phylogenetic diversity (PD) plays an important role in biodiversity, conservation, and evolutionary studies by measuring the diversity of a set of taxa based on their phylogenetic relationships. In phylogenetic trees, a subset of k taxa with maximum PD can be found by a simple and efficient greedy algorithm. However, this algorithmic tractability is lost when considering phylogenetic networks, which incorporate reticulate evolutionary events such as hybridization and horizontal gene transfer. To address this challenge, we introduce PaNDA (Phylogenetic Network Diversity Algorithms), the first software package and interactive graphical user-interface for exploring, visualizing, and maximizing diversity in phylogenetic networks. PaNDA includes a novel algorithm to find a subset of k taxa with maximum diversity, running in polynomial time for networks of bounded scanwidth, a measure of tree-likeness of a network that grows slower than the well-known level measure. This algorithm considers the variant of PD on networks in which the branch lengths of all paths from the root to the selected taxa contribute towards their diversity. We demonstrate the scalability of this algorithm on simulated networks, successfully analyzing level-15 networks with up to 200 taxa in seconds. We also provide a proof-of-concept analysis using a phylogenetic network on Xiphophorus species, illustrating how the tool can support diversity studies based on real genomic data. The software is easily installable and freely available at https://github.com/nholtgrefe/panda. Additionally, we extend the definition of PD to semi-directed phylogenetic networks, which are mixed graphs increasingly used in phylogenetic analysis to model uncertainty of the root location. We prove that finding a subset of k taxa with maximum diversity remains NP-hard on semi-directed networks, but do present a polynomial-time algorithm for networks with bounded level.

network↗