Does boldness stamp a morphological mark? Evidence of decoupling in the invasive giant African snail Lissachatina fulica (Bowdich, 1822).

Half directional total variation algorithm for pulsed electron paramagnetic resonance imaging

Low-rank tensor optimization with total variation regularization for point cloud denoising

Nanoscale microstructure has a significant impact on the properties of materials, defining the high-temperature mechanical properties of metal alloys for aerospace and automotive applications. Quantifying the three-dimensional composition of a material across interfaces within such microstructure is therefore essential. Here, we report three-dimensional (3D) nanoscale composition quantification across interfaces in an Mg-Al-Ca alloy using scanning transmission electron microscopy-based X-ray energy dispersive spectroscopy. We use this demonstration to evaluate two 3D quantification approaches: (1) absolute quantification employing experimentally calibrated ionization cross-sections and compressed sensing tomography based on second order total variation regularisation and (2) relative quantification based on physical parameters extracted from the MC X-ray programme and tomography using the simultaneous iterative reconstruction technique (SIRT). X-ray absorption and shadowing corrections were integrated with both reconstruction methods. The results offer a methodological demonstration of absorption correction with absolute quantification as well as insight into the differences in absorption correction for CS-TV2 and SIRT. In turn, these findings reveal composition changes immediately at the interfaces between the α-Mg matrix and the intermetallic skeleton microstructure characteristic of Mg-Al-Ca alloys. These advances in microscopy methodology to probe 3D compositional fluctuations at buried interfaces establish a route for quantitative analysis of alloys and materials with surface oxides as well as samples containing several elements with overlapping X-ray absorption edges.

Mitragyna parvifolia (Roxb.) Korth., Rubiaceae, is an economically important timber tree species. To meet the increasing market demand for M. parvifolia, it is necessary to assess genetic diversity within individuals to accelerate genetic improvement. Microsatellites, or simple sequence repeats (SSRs), are the most widely used molecular markers in population genetic studies. The present study estimated genetic variation in M. parvifolia among 20 individuals collected from wild populations using 10 polymorphic SSR markers. Allelic data were used to calculate genetic diversity parameters, including genotype distance, the Shannon index, and pairwise relatedness. The Analysis of molecular variance (AMOVA) was also performed to assess the distribution of genetic variation within and among the individuals. Further genotypic distance analysis revealed a wide range of divergence (6–30), indicating both close kinship and distinct lineages among individuals. The Principal Coordinates Analysis (PCoA) explained 45.97 percent of the total variation across the first three axes, with clear clustering patterns among populations. Another observation from the Analysis of Molecular Variance (AMOVA) showed that most genetic diversity resides within individuals (72 percent), followed by among individuals within populations (16 percent) and among populations (12 percent). Further, the analysis indicated moderate genetic differentiation (FST = 0.124, p less then 0.001), with gene flow estimated at Nm = 1.768, suggesting substantial interpopulation connectivity. Shannons diversity index further supported high within-population diversity (sH = 0.559) compared to among-population variation (sH = 0.155). Pairwise relatedness estimates indicated that most individuals were genetically unrelated, confirming a broad and heterogeneous genetic base. The presence of population-specific alleles and moderate structuring highlights the importance of conserving diverse populations. The genetic diversity of M. parvifolia provides valuable insight for conservation strategies and future genetic improvement programs.

Time-series clustering is a prominent research area with extensive practical applications. Given the complexity and diversity of modern time-series data, this study proposes a novel time-series clustering method based on inter-network heterogeneity. First, each time-series is converted into a network by using two types of time-series segmentation techniques. Second, an inter-network clustering approach based on graph spectral theory is introduced: we calculate the total variation (TV) distance between the empirical spectral distributions of each network and identify distinct clusters using a hierarchical clustering algorithm. From the perspective of symmetry, networks constructed from similar time-series tend to exhibit comparable spectral structures, which reflect the underlying structural symmetries of their dynamics. Differences in spectral distributions correspond to symmetry breaking among networks, providing an effective mechanism for distinguishing heterogeneous time-series patterns. Our method effectively preserves more distinctive features inherent in the original time-series. To evaluate the performance of the proposed method, simulation studies are conducted, including the recognition of both stationary and non-stationary sequences. The method also performs well on real-world datasets, such as stock closing prices. These results demonstrate that our approach can handle non-stationary sequences and identify the intrinsic correlations in time-series.

Rice (Oryza sativa L.) is a vital staple crop that nourishes more than half of the global population, especially in Asia. To meet the rising food demand, improving rice productivity and adaptability by effectively utilizing the genetic diversity present in germplasm accessions is essential. In this study, 300 rice germplasm accessions representing diverse sub-groups of O. sativa were evaluated during kharif 2023 at two locations, Mandya and Gangavathi, using an alpha lattice design. Phenotypic observations were recorded for 9 agro-morphological traits. Principal component analysis (PCA) was used to assess genetic diversity and identify key traits contributing to total variation. At Mandya and Gangavathi, the first 4 principal components (PCs) with eigenvalues greater than one accounted for 64.35 % and 69.01 % of the total variability, respectively. Traits such as days to 50 % flowering, productive tillers (PT), spikelet fertility (SF) and panicle weight (PW) were major contributors to variability across locations. Principal component analysis biplots effectively visualized trait interrelationships and enabled the identification of superior-performing germplasm accessions. Germplasms located near a trait vector within the same quadrant of a PCA biplot are considered superior performers for those specific traits. Accessions such as IRGC 898, IRGC 1841 and IRGC 1022 were ranked higher based on their position and alignment with key trait vectors in the biplot. These accessions can be utilised as donor parental lines for combination breeding programs and for developing biparental mapping populations to identify Quantitative Trait Loci (QTLs) linked to specific traits.

Radial Hilbert derivative based total variation penalty for image reconstruction

This study aimed to assess the genetic variability in Aegle marmelos Correa to develop trait-specific genotypes based on morphological and qualitative traits. The evaluation focused on both morphological and qualitative characteristics within the gene pool of this species. High phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were observed for traits such as shell weight, fruit weight, and pulp weight, indicating substantial genetic diversity and strong potential for selective breeding within the germplasm. Heritability estimates ranged widely, with fruit weight showing a low 0.07% and shell weight a high 92.23%, reflecting the significant impact of environmental factors on trait expression. Principal Component Analysis (PCA) revealed that the first principal component (PC1) explained 40.19% of the total variation, with an eigenvalue of 8.12. The first six principal components collectively accounted for 80.77% of total variability. Genotypes CHESB-25 and CHESB-29 exhibited the highest positive PC scores for PC1 and PC2, identifying them as superior selections. Cluster analysis identified six distinct clusters of genotypes, with Cluster V being the largest and Cluster VI the smallest. This clustering highlights the genetic diversity among the bael genotypes and provides a basis for breeding and selection strategies. Cluster IV emerged as the most promising, consistently showing the highest values for key attributes such as shell weight, fruit weight, and fruit yield per plant. Therefore, prioritizing Cluster IV is recommended for selecting superior varieties and developing new cultivars. The study also noted that fruit yield per plant positively correlated with traits like shell weight and fruit weight, emphasizing the importance of these traits for yield improvement. Conversely, negative correlations with seed percent, shell percent, and phenolic content suggest these traits may be less beneficial for enhancing yield. The hierarchical clustering heat map of the 101 bael germplasms offers a detailed perspective on the relationships between various traits and germplasms. The results offer vital information for creating A. marmelos cultivars with higher yields and better quality. For breeding programs, targeted selection is made possible by the discovery of important clusters and superior genotypes (CHESB-25 and CHESB-29). Given the high level of genetic variation found, hybridization may be able to improve desired characteristics like fruit output and weight. Overall, the findings offer important insights for selecting elite genotypes and advancing breeding programs.

The present investigation was conducted to assess genetic variability, correlations among characters, direct and indirect effects and genetic divergence among 20 finger millet genotypes, with the aim of identifying promising lines for yield improvement. The genotypes were evaluated for 12 quantitative traits and their mean performance was recorded. The Analysis of Variance (ANOVA) revealed significant differences among genotypes for all studied traits, indicating sufficient genetic variability in the experimental material. High estimates of Genotypic Coefficient of Variation (GCV) and Phenotypic Coefficient of Variation (PCV) were observed for traits like grain yield per plant (GYPP), number of fingers per ear (NFPE) and ear weight (EW), suggesting potential for genetic improvement. Moderate GCV and PCV were found for traits such as finger length (FL), number of productive tillers and plant height (PH). High heritability (Hbs), coupled with high GA, was observed for GYPP, FL, NFPE, EW and the number of productive tillers per plant (NPTPP), indicating the predominance of additive gene action for these characters. Grain yield per plant showed a significant positive association with EW, NFPE and NPTPP at both phenotypic and genotypic levels. Path coefficient analysis revealed that EW exhibited the highest direct positive effect on GYPP, trailed by NPTPP, FL and NFPE. Mahalanobis D2 analysis divided the genotypes under study into three clusters, with clusters II and III exhibiting the maximum inter-cluster distance. Principal component analysis (PCA) identified the first five components %, which together explained 76.52 % of the total variation, with GYPP, EW, NFPE and FL as major contributors, further validating their importance in selection strategies.

Spatial-temporal variation exists in the density and species composition of malaria-carrying mosquitoes, which will in turn influence the transmission of the disease. While there has been extensive research on seasonality and other main drivers of the vector populations, the heterogeneity partitioned as random effects at various spatial-temporal scales is just as important but has not attracted the same attention. To investigate the relative contributions of the between-house, between-village and between-year variations, as well as other house-level covariates such as inhabitant number and bed net usage on vector density and species composition, intensive pyrethroid spray catches (PSC) sampling was conducted across a 60-month period between 2012 and 2019 from four villages in the Sudano-Sahelian region of Burkina Faso. For density, measured by female Anopheles gambiae s.l. counts, our modelling showed that the between-house variation was the largest variance component, followed by the between-year then between-village variation, after accounting for seasonality and other covariates. Density increased with the number of inhabitants within a household but was uncorrelated with bed net presence. A subset of female mosquitoes was genotyped for species identification, and the composition of An. coluzzii and An. gambiae, the two dominant vectors in the region, varied markedly across villages without an overall trend. The between-village variance contributed up to 76% of the total random variation in species composition, followed by the between-year variance. The between-house variation was statistically insignificant. Neither household size nor bed net usage had any impact on species composition. Interestingly, the between-house component of variation was the largest contributor when measuring mosquito density, but it was the least important for species composition. For between-village variation, the converse was found. Together with the baseline entomological data, the variance components help parameterise potential field trials for novel vector control programmes and monitoring.

Leptolyngbya JSC-1 is a thermophilic and siderophilic cyanobacterium inhabiting iron-rich hot springs. Response surface method (RSM) is being reported for the first time for optimizing growth conditions of this thermophilic and siderophilic cyanobacterium. Using response surface quadratic model of Box-Behnken design, optimal culture conditions (A: temperature, 45°C; B: Fe concentration, 42 µM; and C: light intensity, 2000lx which is equivalent to 27 µmol photons m⁻² s⁻¹ intensity of cool white fluorescent lamp) were determined. The significant model terms were found to be B, AB, A2, B2, and C2. The model R2 value (coefficient of determination) was 0.939, suggesting that the fitted model could explain 93.9% of the total variation. Both the predicted response (OD730 = 2.133) and experimental response (OD730 = 2.1) were in proximity, suggested the appropriateness of the model and RSM. Moreover, an unusual inverse proportion was observed between the Fe concentration and ROS generation with the least ROS generation in JSC-1 grown with 42 µM Fe concentration. Hence, RSM allows evaluating the effects of multiple factors and their interactions on one or more response variables and is recommended to be used for multifactorial optimization studies.

A small azobenzene photoswitch molecule (KIO-301), designed to confer light responsiveness to retinal ganglion cells, was evaluated for safety and feasibility in a first-in-human, phase 1, gene-agnostic, open-label, dose-escalation clinical trial in individuals with advanced retinitis pigmentosa (RP). KIO-301 was administered by intravitreal injection to 12 eyes of 6 participants. The primary outcome was ocular and systemic safety over 30 days. Secondary and exploratory assessments included functional vision testing, visual acuity, kinetic visual field, functional magnetic resonance imaging and participant-reported outcomes. The primary safety outcome was met, with no serious adverse events or dose-limiting toxicities observed at any point. No drug-related intraocular inflammation occurred, and all ocular adverse events were mild and procedure-related. Exploratory assessments identified variation in light perception and functional vision measures in some participants. Light-evoked blood-oxygen-level-dependent signal changes in visual cortical regions were observed following dosing and showed a temporal pattern compatible with pharmacodynamic activity. Participant-reported quality-of-life scores varied over time. In this small, nonrandomized phase 1 study in individuals with late-stage RP, intravitreal KIO-301 demonstrated an acceptable safety and tolerability profile, supporting the feasibility of photoswitch therapy in advanced RP, and motivating further evaluation in larger trials. ClinicalTrials.gov identifier: NCT05282953.

The accurate delineation of mega-environments (MEs) and the rigorous evaluation of test locations are critical for optimizing regional variety trial schemes, particularly when addressing unbalanced datasets from multi-year, multi-location wheat (Triticum aestivum L.) trials. This study aimed at refining the regional wheat trial framework in the Huanghuai Winter Wheat Region (HWWR) of China using an integrated BLUP-GGE biplot approach, which combines best linear unbiased prediction (BLUP) values with genotype main effect plus genotype-by-environment interaction (GGE) biplot analysis to account for temporal variability and experimental error. We systematically evaluated the BLUP-GGE biplot approach, focusing on its goodness of fit and its ability to resolve inter-location relationships. We further assessed test location representativeness, discriminating ability, and overall desirability via the BLUP-GGE biplot, and contrasted ME delineation outcomes between the traditional “which-won-where” polygon method and the test location clustering-based approach. The BLUP-GGE biplot explained 72.9% of total phenotypic variation, with all location vectors displaying positive correlations (maximum angle = 88.8°), confirming the ecological homogeneity of the target region and yielding robust evaluation results. Based on the ideal tester view, Puyang was identified as the most desirable location, followed by Zhumadian, Shangqiu, and Huixian, while Lianyungang and Suqian exhibited relatively poor comprehensive performance. MEs delineated by the “which-won-where” method showed strong inter-ME correlations and insufficient differentiation, whereas the location clustering-based method markedly enhanced inter-ME discrimination (maximum vector angle > 60°), stably partitioning the HWWR into three distinct MEs with clear cultivar–ME interaction patterns: ME1 (Lianyungang, Suqian, Fuyang, Suzhou, Guoyang, Huixian, Huai’an, Xinmaqiao, Huayin, and Yangling), ME2 (Luoyang, Xinxiang, Zhumadian, Shangqiu, Puyang, and Luohe), and ME3 (Baoji, Xuzhou, Yuanyang, Sheyang, and Xingyang). This study confirms the superiority of the BLUP-GGE biplot for analyzing unbalanced multi-year multi-environment trial data and validates a robust clustering strategy for ME delineation. The findings provide a scientific basis for optimizing wheat regional trial systems and facilitating precise cultivar deployment in the HWWR, and offer a reference for analogous studies on other crops or ecological regions.

Ninety-four CIMMYT maize (Zea mays L.) hybrids, along with 6 commercial checks, were evaluated at Kovilpatti, Tamil Nadu, India, during the rabi 2024 and kharif 2025 seasons using an alpha lattice design with two replications to identify major traits associated with grain yield. Significant variability was observed among the hybrids for grain yield and its related traits. Grain yield showed positive association with ear height, number of plants per plot, ear length and ear girth, indicating that ear related traits play a critical role in yield improvement. Path coefficient analysis revealed that number of plants per plot, ear girth and ear length exerted the highest positive direct effects on grain yield, suggesting that these traits can be effectively used as selection criteria in maize breeding programs. Phenotypic correlations followed similartrends but with relatively lower magnitudes, reflecting the influence of environmental factors on trait expression. In contrast, best linear unbiased predictors (BLUP) based correlations were more conservative and provided stable estimates across environments. However, the major trait associations remained consistent across seasons. Hierarchical cluster analysis grouped the hybrids into three distinct clusters, demonstrating substantial variability for grain yield and related traits. Principal component analysis (PCA) further confirmed that ear-related and plant architectural traits contributed significantly to the total phenotypic variation. Overall, integrating BLUP based correlation, path coefficient and multivariate analyses enhances selection efficiency and accelerates genetic gain in multi-environment maize breedingprograms.

X-ray interferometry is an emerging imaging modality with a wide variety of potential clinical applications, including lung imaging. A grating interferometer uses a diffraction grating to produce a periodic interference pattern and measures how a patient or sample perturbs the pattern, producing three unique images that highlight X-ray absorption, refraction, and small angle scattering, known as the attenuation, differential-phase, and dark-field images, respectively. Inaccuracies in grating position and multi-harmonic fringes produce Moiré artifacts when assuming the fringe pattern is perfectly sinusoidal and the phase steps are evenly spaced. We have developed an image recovery algorithm that estimates the true phase stepping positions using multiple harmonics and total variation regularization, removing the Moiré artifacts present in the attenuation, differential-phase, and dark-field images. We demonstrate the algorithm's utility for the Talbot-Lau and Modulated Phase Grating Interferometers by imaging multiple samples, including PMMA microspheres and a euthanized mouse.

The study assessed 109 soybean genotypes including 105 exotic bold-seeded accessions and four checks to identify promising donors for vegetable soybean breeding under Indian conditions. A wide range of variability was observed for yield and related traits with 80 accessions categorised as bold seeded (>13 g/100 seeds) following distinctness, uniformity and stability (DUS) criteria. Hierarchical cluster analysis grouped the genotypes into three clusters with Cluster III showing superior combinations of bold seed size and yield. Principal component analysis explained 73.2 % of total variation and PC2 was closely associated with productivity and seed weight. Positive associations among yield traits and test weight while negative relationships with maturity traits indicated opportunities for simultaneous improvement. Using the multi-trait genotype-ideotype distance index (MGIDI), 11 superior genotypes (G34, G105, G68, G29, G65, G44, G42, G26, G104, G50, G7) were identified combining bold seed size, earliness and high yield potential. These findings provide valuable direction for breeding bold-seeded, high-yielding vegetable soybean cultivars suited to Indian conditions.

Abstract Exploring genetic variation and screening for disease resistance is an important step in crop breeding initiatives but is lacking for many bean varieties, including mung bean. A study was conducted to evaluate morpho-agronomic traits and screen mung bean genotypes for resistance to powdery mildew disease. A total of 132 mung bean and one rice bean (C3) (as check) genotypes were evaluated in an augmented incomplete block design across two cropping seasons. Diversity was evaluated across 40 morpho-agronomic traits, comprising 13 quantitative traits. Qualitative traits were summarized using pivot tables, while variation in quantitative traits was investigated using linear models, principal component analysis (PCA), and agglomerative hierarchical clustering (AHC). The genotypes displayed wide variation for the majority of the traits evaluated, and significant differences were observed among the genotypes, block effects, and seasons. Similarly, the effects due to genotypes, checks and genotypes and checks were significant. One mung bean (G32) genotype and one rice bean (R200) genotype presented resistance to powdery mildew under field conditions. Among the quantitative traits evaluated, only days to maturity and shelling percentage showed statistically significant negative correlations with disease severity. PCA revealed that the first four PCs explained 60.64% of the total variation among the genotypes studied while cluster analysis grouped all the genotypes into four major clusters. These findings underscore the potential for exploiting mung bean diversity in breeding programs aimed at disease resistance, improving yield and agronomic performance. This study provides a foundation for developing improved mung bean varieties, contributing to enhanced food security.

Introduction Browntop millet ( Urochloa ramosa L.) is a climate-resilient crop valued for its tolerance to harsh environments and high nutritional quality. However, limited genetic improvement has restricted its wider utilization. This study aimed to assess genetic diversity and identify trait-specific donors for enhanced agronomic performance. Material and methods A diversity panel of 121 accessions of U. ramosa was evaluated for key agronomic traits. Genetic parameters including variability, heritability, and genetic advance were estimated. Correlation and path coefficient analyses were performed to determine trait associations. Principal component analysis (PCA), cluster analysis, and multi-trait genotype–ideotype distance index (MGIDI) were employed to identify superior genotypes. Results Significant genotypic variation was observed across all traits. Grain yield and fodder yield exhibited high heritability and genetic advance, indicating additive gene action. Correlation and path analysis revealed strong positive associations between yield and contributing traits. PCA showed that the first five components explained 68.66% of total variation. Cluster analysis grouped genotypes into four clusters, with Cluster IV containing superior accessions. MGIDI analysis identified 11 promising genotypes, including GPUBT 6, VBT 004, TNAU 164, IC 613548, IC 613553, TNAU 129, TNAU 110, TNAU 134, TNAU 140, HBr 2, and TNAU 150. Conclusion The study highlights substantial genetic variability in browntop millet and identifies promising genotypes for dual-purpose improvement. These findings provide a foundation for developing high-yielding, early-maturing, and climate-resilient cultivars.

Grain number per spike (GNS) is a key component of wheat yield, yet its genetic architecture remains incompletely understood. This study phenotyped 610 wheat accessions for GNS in four environments and genotyped them using 429,721 single nucleotide polymorphisms (SNPs). The phenotypes were associated with the SNPs using a three-variance multi-locus random-SNP-effect mixed linear model (3VmrMLM) to identify quantitative trait nucleotides (QTNs), as well as QTN-by-environment (QEI) and QTN-by-QTN (QQI) interactions. These genetic components and residual error explained approximately 18%, 31%, 28%, and 23% of the phenotypic variance, respectively. Two and one previously reported genes were found around QTNs and QEIs, respectively. Bioinformatics and haplotype analyses subsequently yielded 25 candidate genes, 22 gene-by-environment interactions (GEIs), and 24 gene-by-gene interactions (GGIs) around the QTNs, QEIs, and QQIs, respectively. Notably, TraesCS1D01G280000, the wheat homolog of OsRopGEF10, was located near a major QTN explaining over 10% of the total phenotypic variation. A gene interaction network constructed from all identified genes highlighted the central role of GGIs in GNS regulation. Environmental variation may reshape the regulatory network through GEIs. Furthermore, superior haplotypes of 12 candidate genes were identified, providing valuable targets for improving wheat yield. Overall, this study dissects the genetic architecture of GNS and offers practical resources for wheat molecular breeding.