Language production is known to operate on different levels of representation. ‘Flow’ in writing results from parallelism in the coordination of subsequent planning units. In this article we discuss three points arising from Roeser et al. (2025b): (1) parallel processing results in non-additive effects; (2) study of the production of multisentence texts permits testing of questions around how language production is co-ordinated in real time, and, more generally; and (3) statistical models must closely align with what we know about the cognitive process of what is being studied.

Influence of Methionine Oxidation on Protein Stability and Association Studied by Free Energy Simulations.

Abstract Norway spruce, an ecologically and economically important conifer species, requires efficient propagation methods for mass production and for use in breeding programs. This review explores several propagation methods, including seed-based and vegetative approaches, with a particular emphasis on the cutting method. It examines key factors affecting rooting success, such as donor tree age, seasonal sampling effects, sample position within the crown, and surrounding rooting conditions. Unlike seed-based propagation, which faces major limitations, including long maturation times, irregular seeding year, and genetic variability, vegetative propagation methods can overcome the mentioned challenges. Vegetative propagation using the cutting method offers advantages such as genetic uniformity, higher genetic gain, and faster regeneration. Nonetheless, compared to seed-based propagation, its higher cost and obstacles, such as reduced rooting success in older trees and plagiotropism of cuttings must be considered. Hedging, serial propagation, and selecting the optimal sampling position within the crown can help overcome these constraints, and enhance rooting success. Achieving an acceptable rooting success rate even in older Norway spruce trees, and even without applying auxin hormones, presents a unique opportunity for propagating mature trees in breeding programs, especially for traits influenced by both additive and non-additive genetic effects. In addition, both additive and non-additive genetic effects can also be utilized through seed-based propagation methods, such as a complete diallel cross, which involves crossing all parental trees in every possible two-way combination and testing their progenies, leading to enhanced genetic gain. Somatic embryogenesis is an alternative propagation method that enables the long-term cryopreservation of cell lines and their mass propagation after evaluating the regenerated seedlings. By integrating different propagation methods, including cuttings, somatic embryogenesis, and seeds, Norway spruce breeding programs can be accelerated, enabling the efficient production and deployment of high-quality planting stock for both reforestation and breeding purposes.

The N-benzyloxycarbonyl-l-alanine molecule is a derivative of the l-α-alanine amino acid with a benzyl carbamate protecting group at the N-terminus, more commonly denoted Cbz-Ala or Z-Ala. In this computational investigation, we sought to determine the available isomers of Z-Ala and their distinguishing spectroscopic signatures via quantum-chemistry methods. Sixty-five total isomers were obtained, and coupled-cluster- and perturbation-theory-based relative energies were computed. The two nearly degenerate, lowest-energy isomers were found to differ in their configuration than the lowest-energy form of isolated alanine, suggesting that the protecting group changes the dominant form of Ala. Through comparisons to exhaustive sampling of Ala and benzyl formate isomers, nearly all of the Z-Ala structures could be ascribed to fragment-paired structural motifs, with a few outliers exhibiting new intramolecular interactions between the constituent fragments. Based on this observation, an assessment of the additivity of the two fragments' relative energies was performed for Z-Ala energies. Many of the isomers' energies were reasonably described by such considerations, although backbone strain and hydrogen-bonding interactions altered this energy landscape and led to nonadditive effects for several of the isomers. Comparison to experimental REMPI-based UV/IR ion-dip vibrational spectra in the 90-1822 cm-1 region indicated that two isomers are dominantly present at the experimental conditions, although signatures of other isomers from the ensemble were also observed. Clear assignments of structural motifs were possible through this experimental comparison. Computed coupled-cluster benchmarks allowed for methodology assessments in this study. The modified opposite-spin MP2 method (MOS-RI-MP2) was found to be particularly accurate, relative to these benchmarks, after minor adjustment of the range-separation parameter. Density functional theory (DFT) methods were found to be variable in their accuracy for both energies and spectra, although a few key functionals performed particularly well for this system in the low-frequency region of the vibrational spectrum. These methodology constraints provided recommendations for similar systems and subsequent anharmonic analyses.

Dynamics of leaf litter decomposition and nutrient release in mangroves under different control conditions: Highlighting the litter quality, decomposer and mixing effect.

Genetic factors significantly influence the timing of menarche and menopause, key markers defining the female reproductive lifespan. However, prior genetic studies have primarily focused on additive genetic models, overlooking potential gene-gene interactions ('epistasis'), particularly within critical hormonal pathways such as follicle-stimulating hormone (FSH) signaling. This study investigates epistasis between common genetic variants in the FSH β-subunit gene (FSHB) and the FSH receptor gene (FSHR), by analyzing their combined impact on female reproductive lifespan. Using data from the UK Biobank, we performed genetic association analysis including 124,336 White British women with reproductive lifespan data, and 36,478 with menstrual cycle length data. Participants were stratified according to genotypes at FSHB rs11031006 (G>A) and FSHR rs6166 (C>T), and associations with reproductive lifespan and menstrual cycle length (categorized as < 26, 26-28, >28 days) were tested using linear and ordinal logistic regression, including sub-stratified models to assess non-additive (epistatic) effects. In rs11031006 AA homozygotes, rs6166 C allele dosage was associated with longer menstrual cycles (P = 3.79 × 10-2; OR = 1.24) and an extended reproductive lifespan of up to 7 months (P = 3.57 × 10-2). No significant effects were observed in rs11031006 G allele carriers, revealing a genotype-dependent epistatic interaction. By examining variants of the FSH pathway, we demonstrate how subtle changes in hormone production and receptor responsiveness can interact to yield significant biological effects on menstrual cycle dynamics, ovarian aging, and female reproductive lifespan-ultimately highlighting the need to move beyond purely additive genetic models in reproductive genetics.

Soybean is a globally important economic and food crop, whose production is often constrained by drought stress, posing a serious threat to yield and quality. Genomic selection (GS) has become a core technology in modern breeding, effectively enhancing breeding efficiency. However, conventional prediction models mainly rely on additive genetic effects and fail to adequately incorporate non-additive factors such as epistasis, limiting further improvements in prediction accuracy. In this study, a genome-wide epistatic analysis of soybean drought tolerance identified 3594 protective interaction pairs. Incorporating significant epistatic SNP pairs into six genomic prediction models resulted in comparable and substantial improvements in prediction accuracy across all models (by 24%). Furthermore, integration of AlphaFold2-based protein structure prediction and transcriptional regulatory analyses validated the biological reliability of protective epistatic pairs, effectively reducing the risk of false positives. Network construction and functional enrichment analyses further revealed that these epistatic pairs participate in coordinated protein structural interactions and are enriched in key biological pathways. Haplotype analysis confirmed the critical regulatory role of non-additive effects in soybean drought tolerance. Collectively, this study establishes a comprehensive evidence chain from molecular mechanisms to breeding applications, demonstrating that integrating epistasis into GS can effectively enhance prediction performance for drought tolerance in soybean. These findings provide novel research strategies for the genetic analysis of complex traits and efficient breeding.

Genomic selection (GS) estimates the GEBV from genome-wide markers to reduce generation intervals and optimize germplasm selection, which is particularly advantageous for high-cost or late-expressed traits. While models like GBLUP are popular, they assume a polygenic architecture. In contrast, the Bayesian alphabet and machine learning (ML) can accommodate other types of genetic architectures. Given that no single model is universally optimal, stacking ensembles, which train a meta-model using predictions from diverse base learners, emerge as a compelling solution. However, the application of stacking in GS often overlooks non-additive effects. This study evaluated different stacking configurations for genomic prediction across 10 simulated traits, covering additive, dominance, and epistatic genetic architectures. A 5-fold cross-validation scheme was used to assess predictive ability and other evaluation metrics. The stacking approach demonstrated superior predictive ability in all scenarios. Gains were especially pronounced in complex architectures (100 QTLs, h2 = 0.3), reaching an 83% increment over the best individual model (BayesA with dominance), and also in oligogenic scenarios with epistasis (10 QTLs, h2 = 0.6), with a 27.59% gain. The success of stacking was attributed to two key strategies: base learner selection and the use of robust meta-learners (such as principal component or penalized regression) that effectively handled multicollinearity.

Accurately predicting the phenotypic consequences of genetic variation is a major challenge for precision medicine. The problem is exacerbated by epistatic interactions, nonadditive effects between genetic variants that produce unexpected phenotypes. Here, we explore an understudied form of positive epistasis: intragenic complementation, in which pairs of loss-of-function variants restore near wild-type protein function. Using mutational scanning in yeast, we identify thousands of such interactions in a clinically important enzyme, human argininosuccinate lyase (ASL). Restoration of protein function is not due to the biochemical properties of the substituted amino acids, but rather to a structural feature of the protein, the active site assembly. We develop a machine learning algorithm that uses protein language model embeddings to predict intragenic complementation in ASL with 99.6% accuracy. Additionally, the model trained on ASL generalizes to a structurally related but sequence-divergent enzyme, fumarase, with accuracy over 90%. Our findings reveal a structural basis for this form of epistasis and provide a predictive framework that could extend to at least 4% of human proteins.

Apple breeding programs focus on enhancing yield, quality, and disease resistance, with a strong emphasis on evaluating phenological traits like flowering time and pomological traits such as fruit size and flavour, which are crucial for commercial success and consumer preference. Twenty-four families were obtained by crossing six apple varieties selected as pollen receptors and four apple genotypes resistant to scab selected as pollen donors. Data related to bud burst date, flowering date, harvest date, lengths of the periods between bud burst and flowering and from flowering to harvest (developmental period), fruit equatorial and polar diameter, fruit polar/diameter ratio, soluble solid content (SSC) and flesh firmness were analysed as a genetic partial diallel design. The study’s ANOVA on 24 fruit families across two years revealed significant genotype–environment interactions affecting flowering date, harvest date, and developmental periods, with some variables like fruit weight and soluble solids showing consistent variation. During each year, temperature influenced phenological phases, with earlier budbreak and flowering in warmer, less variable conditions in 2019. Analysis of genetic effects indicated high heritability for phenological traits and moderate heritability for fruit morphology and quality, with parental genetic contributions varying over years. Principal component and Procrustes analyses identified key variable groupings and parent profiles, highlighting genotypes such as ‘Granny Smith’, ‘McIntosh’, and ‘HM100’ with consistent additive effects, and certain families with notable heterotic performance. Overall, genetic and environmental interactions significantly shape phenological and fruit quality traits, guiding breeding strategies.

Over the past three decades, rigorous empirical research has highlighted both cumulative and non-additive effects of childhood trauma, which are intricately intertwined with the broader developmental and psychosocial context. Latent class analysis has proven useful in identifying at-risk groups, thereby informing the design of targeted prevention and early-intervention efforts. Extending prior research to highly complex, high-risk children and families receiving intensive home-based treatment (IHBT), this study analyzed archival data from 10,301 Connecticut families enrolled in the Intensive In-Home Child & Adolescent Psychiatric Service (IICAPS) from May 2014 to February 2020. The objective was to identify clusters of child traumatic events alongside familial and community-level adversities that predict treatment engagement within this marginalized and hard-to-reach population. Using latent class analysis (LCA), four classes emerged: (1) Unspecified Adversity (69% probability of membership across the sample); (2) High Family Adversity (13%); (3) High Child Trauma & Family Adversity (11%); and (4) High Child Trauma (7%). Relative to the Unspecified Adversity group (reference class), all other groups exhibited lower odds of completing treatment. These findings hold implications for developing targeted assessment and intervention strategies to enhance treatment engagement and outcomes for underserved youth in intensive home-based programs.

Abstract Background Understanding how plant litter diversity influences soil carbon (C) and nitrogen (N) dynamics is critical for explaining C and N cycling in forest ecosystems. However, the influence of litter functional traits on soil C and N dynamics across different forest soils remains unclear. In this study, we incubated 38 litter mixtures from 10 dominant tree species with contrasting functional traits in soils from natural secondary forests (NSF) and Larix olgensis plantations (LOP) in northeastern China. We measured C and N mineralization, dissolved organic carbon (DOC), and microbial biomass carbon (MBC) to quantify non-additive effects. In addition, we evaluated the effects of litter functional identity (Community-weighted mean, CWM) and functional diversity (Rao’s Q ) on non-additive effects across different soils. Results The non-additive effects of mixed leaf litter on soil C and N dynamics varied across the two soils. The proportion of non-additive effects on C and N mineralization, DOC, and MBC ranged from 17.8% to 60.7% in both soils. Non-additive effects were generally more frequent in NSF soils than in LOP soils, which may be attributed to the higher organic matter content and more diverse microbial communities. Trait-based models showed that functional diversity was the dominant predictor of non-additive effects in NSF soils, explaining 89.3% and 84.3% of the variations in C and N mineralization, respectively. However, in LOP soils, trait effects were more function-specific; functional diversity governed C mineralization, whereas functional identity had a greater influence on N mineralization. Specific traits, such as Rao’s Q of TN and TC, and CWM of TC, emerged as key regulators of non-additive effects. Conclusions Our findings underscore the importance of integrating trait-based frameworks with different forest soils to understand the non-additive effects of litter mixtures. By identifying how specific traits mediate soil C and N dynamics differently across the two soils, this study provides a theoretical basis for predicting biogeochemical responses to litter diversity, with implications for forest biodiversity conservation and soil fertility improvement strategies.

This study was carried out, to estimate some chemical components of fifteen genotypes of pea (Pisum sativum L.) (five parents+ ten crosses) produced using half diallel crossing system implemented by Randomized Complete Block Design (RCBD), repeated three times. The Least Significant Difference test (LSD) at 0.01 significant levels was used to compare means. The following is a summary of the study's findings:The mean squares of genotypes, GCA and SCA were highly significant for all studied traits except GCA mean squares of protein which was only significant. The cross (Avolla× Jeza) showed maximum protein and oil% reached 18.613 and 1.287% respectively, while maximum starch% was 58.403% showed by the cross (America× Jeza). Parent (America) showed the best performance for protein, oil and fiber% which recorded 19.793, 1.737 and 1.577% respectively. The cross (Avolla× Jeza) showed the best value for heterosis and heterobeltosis due to protein%, while the cross (America× Jeza) gave the highest values for heterosis and heterobeltosis for starch. The magnitude of was larger than for all studied traits confirming the importance of non-additive gene effect in controlling the inheritance of these traits. Heritability in broad sense was found to be high for all traits, while it was moderate for protein and ash and low for the other traits.

Abstract The repeated interaction model provides a framework for emulating and analyzing the dynamics of open quantum systems. We explore here the dynamics generated by this protocol in a system that is simultaneously coupled to two baths through noncommuting system operators. One bath is made to couple to nondiagonal elements of the system, thus it induces dissipative dynamics, while the other couples to diagonal elements, and by itself it generates pure dephasing. By solving the problem analytically exactly, we show that when both baths act concurrently, a strong systembath coupling gives rise to nonadditive effects in the dynamics. A prominent signature of this nonadditivity is the characteristic slowing down of population relaxation, driven by the influence of the dephasing bath. Beyond dynamics, we investigate the thermodynamic behavior of the model. Previous studies, using quantum master equations, showed that strong system-bath coupling created bath-cooperativity in this model, allowing heat exchange to the dephasing (diagonally coupled) bath. We find instead that, under the repeated interaction scheme, heat flows exclusively to the dissipative bath (coupled through nondiagonal elements). Our results highlight the need for a deeper understanding of the types of open quantum system dynamics and steady-state phenomena that emerge within the repeated interaction framework and the relation of this protocol to other common open quantum system techniques.

HighlightsWhat are the main findings?•In Madrid children aged 8–12, eating vegetables ≥2/day and fish ≥2–3/week was associated with a lower risk of KIDSCREEN <40; adjusted probabilities: 40.1% (neither), 25.8% (vegetables only), 29.7% (fish only), 34.0% (both). The combined effect was smaller than the sum of the separate effects.•Moderate-to-vigorous physical activity was protective; recreational screen time was detrimental.What are the implications of the main findings?•Two concrete, feasible targets for school canteens and households, alongside more physical activity and less screen time.•Causal validation is needed, with an equity focus by SES and by school.Background/Objectives: Evidence links children’s health-related quality of life (HRQoL) to overall diet, but data on specific, actionable habits are limited. We tested whether vegetable intake ≥2 portions/day and fish intake ≥2–3 times/week were associated with risk of low HRQoL (KIDSCREEN-10 Index score <40) and assessed their joint effect and robustness to overall diet quality. Methods: In three waves (2020–2023) in Madrid (Spain), 1127 observations from 771 children (8–12 years) were analysed. Logistic Generalised Estimating Equations (GEE) adjusted for age, sex, socioeconomic status (four levels), moderate-to-vigorous physical activity (MVPA), screen time, body mass index (BMI) z-score, wave and school ownership. Marginal predicted probabilities were computed for four exposure combinations (neither, vegetables only, fish only, both). Sensitivity models added school area and the Mediterranean Diet Quality Index (KIDMED; KIDMED_wo_FV and total); hybrid within–between GEE and a linear mixed model for continuous KIDSCREEN-10 were also fitted. Results: Vegetables ≥2/day and fish ≥2–3/week were inversely associated with low HRQoL (odds ratio (OR) 0.49 (95% confidence interval (CI) 0.30–0.82) and 0.61 (0.43–0.87)). The interaction was positive (OR 2.50 (1.39–4.53)). Adjusted probabilities were 40.1% (neither), 25.8% (vegetables only; −14.3 percentage points (p.p.)), 29.7% (fish only; −10.5 p.p.), and 34.0% (both; −6.1 p.p.). Findings persisted with KIDMED_wo_FV and attenuated with total KIDMED. MVPA related inversely and screen time directly to risk. Conclusions: Vegetables ≥2/day and fish ≥2–3/week were associated with lower odds of low HRQoL, with non-additive combined effects. These simple targets may complement physical-activity promotion and reduced screen time; longitudinal/experimental studies should test causality and dose–response.

To investigate how different forms of chromosomal imbalance affect human embryo morphokinetics, using a dataset of PGT-A tested embryos and a robust statistical framework that accounts for patient- and cycle-level variability. This retrospective cohort study included 1303 embryos from 525 ICSI-PGT-A cycles from a single centre. Embryos were categorized by aneuploidy type as euploid, single, double, or complex (≥ 3) and further subtyped by chromosomal configuration. Developmental timings were extracted from time-lapse monitoring and compared using linear mixed-effects models with random intercepts for patient and treatment cycle. Estimated marginal means and pairwise contrasts were calculated for each morphokinetic parameter (tPB2 to tEB) and key developmental intervals (tSC → tB, tM → tEB). Morphokinetic behavior varied according to chromosomal load and aneuploidy type. Single aneuploidies, particularly monosomies, showed a biphasic delay pattern, with subtle slowing during early cleavage (tPNf-t2) and more pronounced divergence during blastulation (tSB-tEB). Double aneuploidies demonstrated partial early compensation followed by late-stage deceleration, suggesting non-additive or adaptive effects. Complex aneuploidies, and especially complex mosaic embryos, exhibited global and cumulative delays across nearly all stages, reflecting a progressive loss of developmental synchrony with increasing genomic imbalance. These findings support a dosage-dependent model of developmental disruption, in which the severity and timing of morphokinetic delay correlate with aneuploidy complexity in a stage-specific and non-linear manner. While not diagnostic on their own, time-lapse imaging may contribute to ploidy risk assessment and help identify embryos that could benefit from biopsy and further evaluation through PGT-A, particularly when integrated with clinical, biomarker, and genomic information.

Non-additive effects of norethisterone and levonorgestrel mixtures on lipid metabolism at environmentally relevant concentrations.

Background: Rapeseed (Brassica napus L.) remains a vital oilseed crop globally, yet local production in many regions, including Pakistan, falls short of rising edible oil demand. Hybrid breeding offers an effective pathway to improve yield and related agronomic traits, but its success relies on understanding the genetic contributions of parental lines. Combining ability and heterosis analyses provide essential insights into additive and non-additive genetic effects, enabling breeders to identify superior parents and productive cross combinations. Objective: To evaluate the general combining ability (GCA), specific combining ability (SCA), and heterosis of Brassica napus genotypes for plant height, primary and secondary branches, 1000-seed weight, and seed yield per plant using a structured line × tester approach. Methods: Five female lines and three male testers were crossed during the 2022–23 season to generate fifteen F₁ hybrids. These hybrids, along with their eight parents, were evaluated the following season under a randomized complete block design with three replications. Data from five quantitative traits were subjected to analysis of variance, followed by estimation of GCA and SCA based on established line × tester procedures. Mid-parent and better-parent heterosis were calculated to assess hybrid vigor. Results: Highly significant differences were observed among treatments for all traits, confirming substantial genetic variability. Parent vs. cross differences were significant for all traits except plant height. The line × tester interaction was significant for all traits. Additive genetic effects predominated for plant height, branching traits, and 1000-seed weight, whereas seed yield was more strongly influenced by non-additive effects. Several hybrids displayed strong positive SCA and expressed significant positive heterosis over the better parent for seed yield, indicating strong hybrid potential. Conclusion: The study identified valuable parental lines and promising hybrid combinations suitable for future rapeseed improvement. The results highlight the effectiveness of line × tester analysis in distinguishing superior combiners and support the development of high-yielding Brassica napus hybrids.

Durum wheat (Triticum durum Desf.) plays a crucial role in Algeria’s agronomic sector, contributing significantly to the economy and food security. This study assesses the genetic effects and combining abilities of key agronomic traits using a 4 × 4 half-diallel mating design. The parents and their six F1 hybrids were evaluated during the 2021–2022 growing season in a randomized complete block design with three replications at INRAA Institute (Setif, Algeria). Eight morpho-agronomic traits —plant height (PH), spike length (SL), spikes weight (SW), number of spike plant−1 (NS), number of grains spike−1 (NGS), thousand kernel weight (TKW), grain yield (GY) and above-ground biomass (BIO)—were analyzed. Combining ability was assessed using GRIFFING’s Method 2, Model 1 to estimate the general combining ability (gca) and specific combining ability (sca) effects. Significant gca and sca effects were observed across all traits, confirming both additive and non-additive genetic influences. Additive gene effects predominated for PH, SL, and GY, while non-additive effects were more relevant for SW, NS, NGS, TKW, and BIO. Waha (P2) and Beni Mestina (P3) showed significant gca effects for PH, Achouri (P1) for SL and BIO, Beni Mestina (P3) for SW, NS, GY, and MBB (P4) for TKW, indicating their potential as good general combiners. Hybrids Achouri × Waha (H1) and Beni Mestina × MBB (H6) exhibited significant sca effects for PH, while Achouri × Waha (H1) also influenced SL and BIO. Additionally, Beni Mestina × MBB (H6), Achouri × MBB (H3), and Achouri × Waha (H1) exhibited notable sca effects for TKW, indicating complementary gene interactions. These findings provide insights into durum wheat’s genetic architecture, aiding in the selection of promising parents and hybrids for breeding programs. The results emphasize the complexity of hybrid performance prediction and highlight the importance of careful parental selection to enhance yield and related traits.

BackgroundEar diameter (ED) is a key agronomic trait that significantly influences maize yield and is regulated by both genetic and environmental factors.ResultsWe systematically dissected the genetic basis of ED using a diverse multi-parent population of 858 recombinant inbred lines (RILs). Through an integrated approach combining quantitative trait locus (QTL) mapping and genome-wide association study (GWAS), we identified multiple stable loci associated with ED. Notably, we pinpointed two novel and significant loci on chromosomes 5 and 7, which harbor key candidate genes: Zm00001d020000, encoding a phosphatidate cytidylyltransferase involved in membrane biosynthesis, and Zm00001d016356, a putative Kinesin-like protein potentially regulating cell division. Functional annotation, haplotype, and protein structural analyses support their roles in regulating ear development. A comprehensive comparison of 13 genomic selection (GS) models, which demonstrated that the non-linear support vector regression (SVR) model achieved superior predictive accuracy. This highlights the substantial contribution of non-additive genetic effects, such as epistasis, to ED. Furthermore, we identified 45 elite RILs with stable performance across environments, providing valuable breeding materials.ConclusionsOur study unveils novel SNPs and candidate genes, elucidated the complex genetic architecture underlying maize ear diameter. We demonstrate that integrating GWAS and linkage mapping with advanced GS models provides a powerful strategy to dissect complex traits and deliver practical resources for accelerating yield improvement in maize breeding programs. The candidate genes identified represent promising targets for future functional validation.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12864-025-12417-9.