Extent Of Variation Research Articles

Metabolic profiling of Perilla leaves of different colors: insights into metabolite variation and bioactive compound distribution

BackgroundPerilla frutescens (L.) Britt. (Lamiaceae) leaves are essential culinary and medicinal herbs, native to East Asian countries. Three types of Perilla leaves, including green, green/purple and purple, are mainly found, and the leaf color is a critical attribute for consumer preferences. However, the extent of diversity and variability of metabolites in Perilla leaves of different colors remain elusive. Thus, in this study, we applied LC-QqQ-MS (liquid chromatography triple quadrupole tandem mass spectrometry)-based widely targeted metabolic profiling to investigate variation in the metabolite profiles of green (PF1), green/purple (PF2) and purple (PF3) Perilla leaves.ResultsWe qualitatively and quantitatively identified a total of 1239 metabolites in Perilla leaves of different colors. Amino acids and derivatives (15.50%), flavonoids (14.77%), phenolic acids (12.19%), lipids (9.60%), carbohydrates and derivatives (8.47%), organic acids and derivatives (7.99%), nucleotides and derivatives (7.99%), and terpenoids (5.25%) were the major classes of metabolites in perilla leaves. In contrast to green leaves, the principal component analysis and correlation analysis revealed a considerable influence of genotypes for the variation in purple leaves’ metabolite profiles. Differentially accumulated metabolites (DAMs) analysis revealed that flavonoids, phenolic acids, and amino acids and derivatives were the major DAMs, and the phenylpropanoid pathway was the most differentially regulated. All DAMs, including four potential metabolic discriminative biomarkers, were screened out. In addition, we revealed the accumulation patterns of bioactive compounds in the leaves of different colors. It was noteworthy that most of the differentially accumulated flavonoids showed a higher relative content in PF3, followed by PF2 and PF1. Glycosylated cyanidins and delphinidins were identified as the key compounds responsible for the purple coloration of leaves. Finally, we found that the variation in glutamate metabolism may be one of the main contributors for variation in metabolite profiles and biological attributes of Perilla leaves of different colors.ConclusionOur results showed that the metabolic processes, mainly phenylpropanoid biosynthesis and amino acid metabolism, are differently regulated in green, green/purple and purple Perilla leaves. Furthermore, they offer valuable data for a comprehensive use of Perilla leaves and exploring gene-metabolites interactions in Perilla leaves.

Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI.

Goal: Structural brain graphs are conventionally limited to defining nodes as gray matter regions from an atlas, with edges reflecting the density of axonal projections between pairs of nodes. Here we explicitly model the entire set of voxels within a brain mask as nodes of high-resolution, subject-specific graphs. Methods: We define the strength of local voxel-to-voxel connections using diffusion tensors and orientation distribution functions derived from diffusion MRI data. We study the graphs' Laplacian spectral properties on data from the Human Connectome Project. We then assess the extent of inter-subject variability of the Laplacian eigenmodes via a procrustes validation scheme. Finally, we demonstrate the extent to which functional MRI data are shaped by the underlying anatomical structure via graph signal processing. Results: The graph Laplacian eigenmodes manifest highly resolved spatial profiles, reflecting distributed patterns that correspond to major white matter pathways. We show that the intrinsic dimensionality of the eigenspace of such high-resolution graphs is only a mere fraction of the graph dimensions. By projecting task and resting-state data on low-frequency graph Laplacian eigenmodes, we show that brain activity can be well approximated by a small subset of low-frequency components. Conclusions: The proposed graphs open new avenues in studying the brain, be it, by exploring their organisational properties via graph or spectral graph theory, or by treating them as the scaffold on which brain function is observed at the individual level.

Genetic analysis in traditional rice landraces of southern india for puffing quality

The present investigation was carried out at Department of Plant Breeding and Genetics, Agricultural College and Research Institute, Tamil Nadu Agricultural University, Madurai during 2021 - 2022. The study comprising of 23 traditional rive varieties or landraces of Southern India were analyzed for 16 quality traits to study the extent of variability and character association. Norungan, Navarai, Chinnar and Thooyamalli are the landraces that had maximum value for amylose content, expansion volume, expansion ratio, bulk density and puffing yield. High PCV and GCV were observed for the traits bulk density, expansion of volume, expansion ratio and puffing yield. High heritability coupled with high genetic advance as per cent of mean recorded for most of the traits except hulling percentage, milling percentage and head rice recovery. This proved that these traits are governed by additive gene effects and the process of selection would be effective to improve these traits. Character association studies revealed that the improvement of the traits viz., kernel length, hulling percentage, fat content, gelatinization temperature, gel consistency and bulk density are the key parameters to avail good puffing quality. Based on the overall performance, the genotypes Norungan, Navarai, Chinnar and Thooyamalli can be selected for future breeding programme to obtain new recombinant types for premium quality puffing. Bangladesh J. Bot. 53(4): 981-990, 2024 (December)

Studies on Genetic Variability, Heritability and Genetic Advance in Fennel (Foeniculum vulgare Mill.)

The present investigation was carried out with a view to study the extent of variability, heritability in broad sense, genetic advance in per cent of mean in fennel during Rabi season 2018-19 in Augmented Block Design. Plant spacing of 60cm x 40cm and each genotypes were grown in the plot size of 2.0 m x 1.2 m. The observations were recorded on ten characters. In Analysis of variance, the variances due to checks and genotypes were highly significant for all the characters, while, the variance due to blocks were significant for days to 50%flowering, number of umbels per plant and number of umbellets per umbel. Based on mean performance NDF-1, NDF-2, NDF-58, NDF-59, NDF-55, NDF-60 and NDF-76 were observed as significant and most promising genotypes for seed yield per plant and its component traits. The estimates of phenotypic coefficients of variation (PCV) were higher than genotypic coefficient of variation (GCV) for all traits. High magnitudes of variability PCV and GCV were observed in case of seed yield per plant (37.12, 36.95) followed by number of umbels per plant (30.89, 29.78), number of branches per plant (28.25, 22.92), weight of grains per umbel (26.02, 24.01) and number of umbellets per umbel (21.29,19.80). High heritability was observed for all the characters except number of branches per plant (65.83%) having moderate heritability. While, high genetic advance in per cent of mean was estimated for seed yield per plant (75.76) followed by number of umbels per plant (59.13). The high heritability coupled with high genetic advance in per cent of mean was estimated for maximum characters which indicated opportunity for selection response.

Spatio-temporal rainfall variability and trends using a Kriging-interpolation and Innovative trend analysis approach: the case of Wolaita zone, south Ethiopia

Climate change is one of the worst environmental issues, with a negative impact on most developing countries across the globe and in some regions, including Ethiopia. This study seeks to establish the temporal and spatial changes in rainfall for the period 1987–2021. In this study, ordinary statistical measures, such as the mean and coefficient of variation, precipitation concentration index (PCI), and standardized anomaly index (SAI), were applied to investigate the rainfall variation. Concerning the estimation of the spatiotemporal distribution and magnitude of changes in, non-parametric Mann-Kendall (MK) tests, Sen’s slope estimator, and innovative trend analysis (ITA) were also conducted in ArcGIS 10.8 environment and XLSTAT/R. The study showed significant fluctuations in rainfall in the Wolaita zone, with minimum mean annual rainfall in 1997 and maximum rainfall in 2003. All but the Belg season had more negative seasonal anomalies than positive ones. The annual rainfall for each of the AEZs in different parts of the country ranged from one another; it was 969.03 mm in a southeast lowland AEZ and 1648.75 mm in the northwest highland AEZ. Rainfall was not uniformly distributed throughout the year and study area; the highland AEZs received more rainfall in the Belg and Kermit seasons than in the lowland seasons. In the ordinary kriging results, the extent of variability in the CV of the mean annual rainfall for each zone was identifiable. The southwest lowland AEZ produced a CV of 24.22% with a decrease in rainfall amounts, and the northeast highland AEZ produced a CV of 31.63% of the rainfall distribution amounts. This area includes lowland and highland AEZs of the northeastern part of the study area’s rainfall, which is moderately distributed by PCI. This information is helpful when attempting to associate development and cropping systems with temporal and spatial climatic patterns with respect to rainfall for agro-climatological activities and projects or flood regulatory measures.

Assessment of Genetic Variability and Trait Associations in Chickpea (Cicer arietinum L.) Germplasms for Targeted Breeding Strategies

Chickpea (Cicer arietinum L.) is a pivotal legume crop with substantial contributions to global food security and agricultural sustainability due to its high protein content, adaptability and nitrogen-fixing ability. The efficiency of breeding programs in enhancing chickpea productivity and resilience relies heavily on understanding and utilizing genetic variability within the germplasm. This study investigates the extent of genetic variability among forty-three chickpea genotypes, focusing on key traits such as number of pods (NPP), biological yield (BY), harvest index (HI), days to flowering (DTF), days to maturity (DTM), that influence yield and other agronomic characteristics. Analysis of variance showed significant variability among genotypes for various traits. Higher phenotypic coefficients of variation (PCV) compared to genotypic coefficients (GCV) indicated the presence of environmental influence, particularly for traits like number of pods per plant (NPP) and biological yield (BY). High heritability was observed for days to flowering (DTF) and maturity (DTM), while genetic advance as percent of mean (GAM) varied, indicating additive gene action for some traits. Correlation and path analysis revealed positive relationships between plot yield and traits such as Days to Flowering (DTF), plant height (PH), and harvest index (HI). Principal component analysis (PCA) and cluster analysis indicated significant genetic diversity, grouping genotypes into clusters and identifying top performers like GJG1801 and NBEG 924. This study emphasizes the importance of genetic variability in breeding programs, aiding in the selection of elite genotypes for targeted traits improvement.

Genetic Variability and Character Association Analysis for Yield and Its Related Traits in Rain Fed Lowland Rice (<i>Oryza sativa </i>L.)<i> </i>Genotypes at Teppi and Fogera, Ethiopia

In Ethiopia, rice variety development started very recently and is mainly dependent on the introduction breeding materials in which characterization and understanding of the genetic variability of genotypes is the critical step. Knowledge of the extent of genetic variability and interrelationships of yield and yield components is pre-request for designing an effective selection-based rice improvement programs for evolving high yielding rice genotypes. Therefore, this study was carried out with all objectives of assessing genetic variation for yield and yield related traits in lowland rice genotypes. Thirty-six selected lowland rice genotypes were evaluated in simple lattice design with two replications during 2017 cropping season and data on 15 quantitative morphological traits were collected and subjected to various statistical analysis. Combined analysis of variance across location revealed significant location, genotype and genotype × location interaction effects for the traits evaluated at p ≤0.01. The phenotypic coefficient of variation was high for un fertile tiller per plant (88.16 %), followed by infertile grain per panicle (50.66%), and grain yield (21.57%), and the genotypic coefficient of variation was high for un fertile tiller per plant (50.38 %), followed by infertile grain per panicle (27.22%), and culm length (17.7%). The estimate of broad sense heritability ranges from 15.2% for biological yield to 91.5% for plant height. The expected genetic advance as present of mean varied from 5.42% for biological yield to 59.3% for unfertile tiller per plant. Phenotypic and genotypic correlation revealed grain yield had a a significant and positive association with primary branches per panicle (rp=0.4, rg=0.7), fertile grain per panicle (rp=0.5, rg=0.72) and harvest index (rp=0.76, rg=0.73) at both genotypic and phenotypic. Path coefficient analysis of grain yield revealed that harvest index, total tiller per plant and thousand grain weights showed positive phenotypic direct effect. A Positive direct effect was found for total tiller per plant, harvest index and biological yield at genotypic level. The present study indicated sufficient genetic variability for the characters studied in rain fed low land rice genotype, which will create opportunity for future improvement. To have full scale data, further study at various locations and years with the help of molecular techniques should be done.

Variability Studies in Soybean Genotypes under Different Water Regimes

Soybean (Glycine max L.) is economically one of the most important oilseed crop worldwide which have high protein and oil content. Its productivity is frequently affected due to drought occurrence in India. Variability for root shoot traits under early seedling stage under drought stress may be important selection criteria for development of drought tolerant genotypes. The present investigation was carried out at Net house of Experimental Farm, Mandsaur University, Mandsaur. Sixty genotypes, procured from ICAR-IISR, were used to know the extent of genetic variability under different water regimes [normal, 100 ml (100%), S1, 50 ml (50%), S2, 25 ml (25%) and S3,0 ml (0%)] for root-soot traits and relative leaf water content under early seedling stage. Significant genetic variability was recorded for all the traits among genotypes studied under normal and water stress conditions respectively which showed the presence of ample variability in studied material. High Phenotypic Coefficient of Variation than their corresponding Genotypic Coefficient of Variation for the entire trait under different water regimes denoted influence of environment on these traits. The large genetic variation found in these genotypes may be used to develop varieties with better drought tolerance behavior and used as drought donor lines in drought breeding programs. We measured high heritability for selected characters under non water stress condition (75.05% to 100%) and water stress conditions S1 (81.40% to 100%), S2 (85.69% to 100%) and S3 (92.60% to 99.26%) respectively. Additive gene action interacted to control characters under different water regimes because high broad sense heritability along with high genetic advance as percentage of mean were recorded for most of traits. It is indicating that simple phenotypic selection would be effective for these traits under water stress condition.

Timing of standard chow exposure determines the variability of mouse phenotypic outcomes and gut microbiota profile.

Standard chow diets influence reproducibility in animal model experiments because chows have different nutrient compositions, which can independently influence phenotypes. However, there is little evidence of the role of timing in the extent of variability caused by chow exposure. Here we measured the impact of different diets (5V5M, 5V0G, 2920X and 5058) and timing of exposure (adult exposure (AE), lifetime exposure (LE) and developmental exposure (DE)) on growth and development, metabolic health indicators and gut bacterial microbiota profiles across genetically identical C57BL/6J mice. Diet drove differences in macro- and micronutrient intake for all exposure models. AE had no effect on phenotypic outcomes. However, LE mice exhibited significant sex-dependent diet effects on growth, body weight and body composition. LE effects were mostly absent in the DE model, where mice were exposed to chow differences only from conception to weaning. Both AE and LE models exhibited similar diet-driven beta diversity profiles for the gut bacterial microbiota, with 5058 diet driving the most distinct profile. However, compared with AE, LE effects on beta diversity were sex dependent, and LE mice exhibited nine times more differentially abundant bacterial genera, the majority of which were inversely affected by 2920X and 5058 diets. Our findings demonstrate that LE to different chow diets has the greatest impact on the reproducibility of several experimental measures commonly used in preclinical mouse model studies. Importantly, weaning mice from different diets onto the same diet for maturation may be an effective way to reduce unwanted phenotypic variability among experimental models.

Genetic Variability and Characters Association for Lodging, Yield and Related Traits of Field Pea (Pisum sativum L.) Genotypes in Contrasting Plant Types

A total of 49 field pea genotypes, belonging to two different plant types were evaluated for 13 traits at Bekoji and Kofele in 2020 to assess the extent of genetic variability and association among morpho-agronomic traits. Analysis of variance revealed a highly significant difference between plant types and among the tested genotypes within each plant type for most of traits. Relatively, genotypes within prostrate type are more variable in thousand seed weight and genotypes within semi-leaf less type are more diverse in plant height, ascochyta blight and lodging severity score. Moderate to high genotypic coefficient of variation (GCV), heritability in broad sense (H2b) and genetic advance as a percent of mean (GAM) combination were recorded from seed yield, thousand seed weight, plant height and number of seeds per plant in both plant types. The path and correlation analysis showed that lodging score had significant negative association and negative direct effect on seed yield in prostrate type. Number of seeds per plant and plant height had both positive direct effects and significant positive association with seed yield consistently in both plant types. Thousand seed weight also showed both positive direct effects and significant positive association with seed yield in semi-leaf less type. Therefore, great emphasis should be given for those traits while making selection of field pea genotypes for high seed yield. However, for the improvement of yield and lodging simultaneously there should be balance between yield and lodging related traits especially in prostrate type. The principal component analysis (PCA) indicated that the first five principal component axes accounted for 71.67% of the total variability. Generally, the results suggested the possibility of further improvement of seed yield and associated desired traits through selection in both plant types.

Climate variability and its long-term trends in central Taraba, Nigeria: implications for local ecosystems and rural livelihoods

This study examines the long-term trends of climate variability in Central Taraba, Nigeria, with a focus on analyzing temperature and rainfall patterns from 1980 to 2020. Central Taraba has witnessed notable shifts in climate patterns, characterized by increasing temperatures, erratic rainfall, and prolonged periods of drought and excessive rainfall. These climatic changes pose significant threats to local ecosystems and rural livelihoods, which rely heavily on rain-fed agriculture and natural resources. The research highlights significant fluctuations in both rainfall and temperature, underscoring the challenges posed by climate variability to the region’s ecological and socio-economic systems. The primary objectives of this study are to examine the extent of climate variability on key ecosystem services, assess its impacts on local livelihoods, and identify sustainable management strategies to address these effects. Results show a clear upward trend in both minimum and maximum temperatures across the study area, with some towns experiencing more pronounced increases in minimum temperatures. Rainfall patterns reveal an erratic trend with periods of both drought and excessive rainfall, though a slight but statistically insignificant downward trend in rainfall was observed in most towns. These climatic shifts have profound implications for agriculture, water resources, and rural livelihoods, as communities in the region heavily depend on rain-fed farming and natural ecosystems for sustenance. The study also underscores the pressing need for adaptive strategies such as climate-resilient agriculture and improved water resource management to mitigate the adverse effects of climate variability. Recommendations emphasize strengthening community resilience and integrating climate data into regional development policies to safeguard local livelihoods and ecosystems from future climate-related risks.

The Petrogenesis of Devonian Volcanism and Its Tectonic Significance in the Kalatag Area, Eastern Tianshan, Xinjiang, China

The Kalatag mineralization belt is an important metallogenic belt of polymetallic mineral deposits in the northern part of eastern Tianshan, and its age and tectonic setting are still controversial. We identified a set of Devonian volcanic rocks hosted in the Early Palaeozoic package of dominantly marine sediments with a small amount of terrestrial rocks. This study presents petrological, U–Pb geochronology, and geochemical data for the volcanic rocks. The ages of the rhyolite (407.2 ± 1.9 Ma) and basaltic andesite (380.4 ± 2.8 Ma) suggests that the Kalatag belt is a Devonian volcanic succession. These rocks consist mainly of marine calc–alkaline lava, tuff, pyroclastic rocks, and minor terrestrial basaltic andesite. The lavas are characterized by the enrichment of light rare earth elements and strongly depleted in Nb and Ta, typical of island arc magmatic rocks. The volcanic rocks probably originated from the partial melting of the mafic lower crust which was modified by subducted slab-related fluids. During their ascent through the crust, these volcanic rocks underwent variable extents of fractional crystallization (rhyolites) and crustal contamination (basaltic andesites). Combined with the results of previous studies, we suggest that the Devonian rocks formed in an island arc related to the northward subduction of the Northern Tianshan Ocean with a crustal thickness of ~35–40 km.

Associations between a range of enteric methane emission traits and performance traits in indoor-fed growing cattle.

Despite the multiple definitions currently used to express enteric methane emissions from ruminants, no consensus has been reached on the most appropriate definition. The objective of the present study was to explore alternative trait definitions reflecting animal-level differences in enteric methane emissions in growing cattle. It is likely that no single methane trait definition will be best suited to all intended use cases, but at least knowing the relationships between the different traits may help inform the selection process. The research aimed to understand the complex inter-relationships between traditional and novel methane traits and their association with performance traits across multiple breeds and sexes of cattle; also of interest was the extent of variability in daily enteric methane emissions independent of performance traits like feed intake, growth and liveweight. Methane and carbon dioxide data were collected using the Greenfeed system on 939 growing crossbred cattle from a commercial feedlot. Performance traits including feed intake, feeding behavior, liveweight, live animal ultrasound, subjectively scored skeletal and muscular traits, and slaughter data were also available. A total of 13 different methane traits were generated, including (average) daily methane production, 5 ratio traits and 7 residual methane (RMP) traits. The RMP traits were defined as methane production adjusted statistically for different combinations of the performance traits of energy intake, liveweight, average daily gain, and carcass weight; terms reflecting systematic effects were also included in the fixed effects linear models. Of the performance traits investigated, liveweight and energy intake individually explained more of the variability in methane production than growth rate or fat. All definitions of RMP were strongly phenotypically correlated with each other (>0.90) as well as with methane production itself (>0.86); the RMP traits were also moderately correlated with the methane ratio traits (>0.57). The dataset included heifers, steers, and bulls; bulls were either fed a total mixed ration or ad lib concentrates. When all sexes fed total mixed ration were compared, bulls, on average, emitted the most enteric methane per day of 269.53g, while heifers and steers produced 237.54 and 253.26g, respectively. Breed differences in the methane traits existed, with Limousins, on average, producing the least amount of methane of the breeds investigated. Herefords and Montbéliardes produced 124.50g and 130.77g more methane per day, respectively, than Limousins. The most efficient 10% of test-day records, as defined by daily methane independent of both energy intake and liveweight emitted, on average, 54.60g/d less methane than animals that were average for daily methane independent of both energy intake and liveweight. This equates to 6.5kg less methane production per animal over a 120-d finishing period for the same feed intake and liveweight.

Quantifying systemic vulnerability of interdependent critical infrastructure networks: A case study for volcanic hazards

Infrastructure networks are vital for the communities and industries that rely on their continued operation. Disasters stress these complex networks and can provoke systemic disruptions that extend far beyond the spatial footprint of hazards. An enduring challenge for assessing infrastructure networks within disaster impact assessment frameworks has been to adequately quantify the high spatial interdependence of these networks, and to consider risk management interventions through time. This is of particular importance for volcanic eruptions, which can produce multiple hazards over highly variable spatiotemporal extents. In this study, we present a methodology for the quantification of systemic vulnerability of infrastructure networks, which can be coupled with physical vulnerability models for the purpose of impact assessment. The two-part methodology first quantifies the haard-agnostic criticality of infrastructural components, inclusive of interdependencies, and then incorporates representative hazard spatial footprints to derive the systemic vulnerability. We demonstrate this methodology using the case study of volcanic eruptions from Taranaki Mounga volcano, Aotearoa New Zealand, where there are many industrial sites of national importance, and a high likelihood of a complex multi-hazard volcanic eruption. We find a considerable increase in the systemic vulnerability of electricity and natural gas network components after incorporating infrastructure interdependencies, and a further increase in the systemic vulnerability of these critical components when cross-referenced with potential volcanic hazard spatial extent. The methodology of this study can be applied to other areas of interest in both its hazard-agnostic or hazard-dependent form, and the systemic vulnerability quantification should be incorporated into impact assessment frameworks.

Evaluation of pesticide contamination risks and sustainable practices in Ecuadorian agriculture

Pesticide use is crucial in global agriculture to control pests and increase crop yields. In Ecuador, the use of pesticides, including fungicides and insecticides, is common in crops such as potatoes and tomatoes. However, the indiscriminate use of these products has proven to have negative consequences for human health and the environment. This study investigates these risks in Ecuadorian agriculture, specifically in the Paute canton, where inadequate data exacerbates these issues. Employing a descriptive research design, data were collected through validated surveys administered to 30 farmers (9 women and 21 men) residing in various rural communities. The study also included observational methods to complement survey data. Findings reveal that farmer, predominantly with primary education, face health risks from inappropriate pesticide use. Symptoms reported include eye irritation, headaches, and nasal irritation, which vary based on education level and safety practices. The research highlights the urgent need for improved pesticide management through targeted training and sustainable practices. This study’s insights into the extent of health issues and variability in safety measures provide a basis for developing effective interventions to mitigate the health and environmental impacts of pesticide use in Paute and similar regions.

Exploration on flutter mechanism of a damaged transonic rotor blade using high-fidelity fluid–solid coupling method

Structural damage in turbomachinery is a primary origin of aeronautic accidents, which is receiving increased attention. This study is thus focused on the aeroelastic analysis of damaged blades, including the onset of flutter and underlying mechanisms. First, a high-fidelity fluid–solid coupling system is established with computational fluid dynamics (CFD) and computational structural dynamics (CSD) technologies, via which the dynamic aeroelastic analysis is conducted based on static aeroelastic deformation. Second, a damaged rotor blade is parametrically modelled with variable damage levels, extents, and positions. Finally, the modal identification method of spectral proper orthogonal decomposition (SPOD) is applied to observe flow details and provide physical insight into the flutter mechanism for damaged blades. Numerical analysis finds that there is a critical damage level below which the aeroelastic stability is positively improved with increasing damage level; otherwise, a significant loss of stability is induced. The damage location and extent further affect this critical damage level and the change rate crossing the threshold. The simulation with CFD/CSD finds that the high pressure near the trailing edge induced from boundary layer separation suppresses vibrations in stable conditions, but motivates vibrations during flutter, which is because of the high-pressure spread to nearing blades. SPOD modes reveal that high-frequency disturbances with large scale are primary factors inducing flutter, which is further stimulated by the high-order disturbances with small scale. This study provides a crucial foundation for the fatigue prediction for rotor blades in service and the optimisation design for high-performance turbomachinery in the near future.

Jump around: Selecting Markov Chain Monte Carlo parameters and diagnostics for improved food web model quality and ecosystem representation

Capturing ecological data variability in food web models is an important step for improving model representation of empirical systems. One approach is to use linear inverse modelling and Markov Chain Monte Carlo (LIM-MCMC) techniques to set up an inverse LIM problem using empirical data constraints, and then sample multiple plausible food webs from the inverse problem using an MCMC algorithm. We describe the set of plausible food webs as an ‘ensemble’ of solutions to the inverse problem sampled with the LIM-MCMC algorithm. The extent of data variability eventually integrated into an ensemble depends on how well the LIM-MCMC algorithm samples the solution space. Algorithm quality can be adjusted via user-defined parameters describing starting points, jump sizes, and number of iterations or food webs produced. However, little information exists on how each LIM-MCMC algorithm parameter affects the degree of empirical data variability introduced into the ensemble. Further, post hoc algorithm quality diagnostics with commonly used trace plots and the coefficient of variation (CoV) rarely address critical aspects of algorithm quality, such as (1) if the returned ensemble successfully targeted the solution space distribution (stationarity), (2) correlation between ensemble solutions (mixing), and (3) if the ensemble contains enough solutions to adequately capture input data variability (sampling efficiency). Therefore, we used several established MCMC convergence diagnostics to (1) quantify how algorithm parameters affect ensemble flow values and if these differences propagate to ecological indicators and (2) evaluate algorithm quality and compare to current evaluation and ecosystem modelling methods. We applied 30 LIM-MCMC algorithm combinations of varying starting points, jump sizes, and number of iterations to solve food web ensembles from a single food web model. We analysed ensembles with Ecological Network Analysis (ENA) to calculate indicators describing system function. Results show that LIM-MCMC algorithm parameters, in particular the jump size, affect ensemble flow values, which propagate to ecological indicators describing different ecosystem function of the same model. Thereafter, comparisons of post hoc diagnostics show that MCMC convergence diagnostics provided more robust estimates of algorithm quality than trace plots and CoV. Together, these findings underpin several novel recommendations to enhance LIM-MCMC algorithm parameter selection and quality assessments applicable to any ecological ensemble network study.

Studies on Genetic Divergence in Faba Bean (Vicia faba L.)

Background: The genetic divergence in faba bean genotypes is critical for breeding programs aiming to improve yield and other agronomic traits. Understanding the extent of genetic variability among different genotypes can guide the selection of superior varieties and enhance crop improvement efforts. This study was conducted to evaluate the genetic diversity among 47 faba bean genotypes grown under the specific agro-climatic conditions of Chhattisgarh. Methods: The experiment was conducted during the Rabi season of 2022-23 at the Hi-Tech-Nursery, Dau Kalyan Singh College of Agriculture and Research Station, Bhatapara, under Indira Gandhi Krishi Vishwavidyalaya, Chhattisgarh. A total of 47 faba bean genotypes were evaluated for genetic divergence using Mahalanobis D2 statistics and Tocher’s method. Ten quantitative characters, including marketable pod yield per plant and marketable pod weight appearance, were measured and analyzed. The genotypes were grouped into clusters based on their genetic distances. Result: The clustering pattern indicated that genetic diversity was independent of geographical diversity. Cluster II contained the highest number of genotypes (19), followed by Cluster I (13), while Cluster IV had the least (1). Among the ten quantitative characters studied, marketable pod yield per plant contributed most to genetic divergence, followed by marketable pod weight appearance. The maximum intra-cluster D2 value was observed in Cluster IV (477.35), followed by Cluster III (250.07). The highest inter-cluster D2 value was between Cluster I and Cluster IV (7917.34), followed by Cluster IV and Cluster V (6156.43).

Genetic variability, character association and path analyses effects on fiber yield and yield attributing morpho-agronomic traits of tossa jute (Corchorus olitorius L.)

Jute (Corchorus spp.) is a natural fiber crop having a lower extent of genetic variability due to its self-pollinating nature. The success of any breeding program for variety development primarily depends on the presence of genetic variation in the parental genotypes. A field experiment was conducted in a randomized complete block design at ManikganjStation of Bangladesh Jute Research Institute (BJRI) during 2019-2020. The genetic divergence of 58 tossa jute (C. olitorius) genotypes including two pre-released standard varieties (O-9897, BJRI Tossa pat 4) and 56 accessions were studied to select genotypes having the most divergence in natural rainy condition. The jute genotypes were categorized into five clusters based on Mahalanobis D2 analysis. Cluster I and IV showed maximum inter-cluster distance (181.44) while the maximum intra-cluster distance (16.15) was recorded in cluster I. Cluster V revealed the highest values for number of nodes, stem mid-diameter, core diameter, green weight with leaves, dry stick weight and dry fiber weight per plant while cluster IV showed higher means for plant height, green bark thickness and leaf area. The highest contribution (47.14%) was wielded by plant height of total deviations. The results recommended that hybridization between genotypes of cluster V, IV and III could provide a wide range of discrepancies in the segregating generation which could offer chance for isolation of good genotypes with high fibre yielding lines.

Morphological variability of rosette leaves within Sempervivum ciliosum and S. ruthenicum complexes (Crassulaceae): The geometric morphometrics approach

The yellow-flowered Sempervivum species from the Balkan Peninsula, tentatively divided into Sempervivum ciliosum (S. ciliosum, S. galicicum, S. jakucsii, S. klepa, S. octopodes) and S. ruthenicum complexes (S. kindingeri, S. leucanthum, S. ruthenicum, S. zeleborii), are well known for their pronounced phenotypic plasticity, especially in the vegetative parts, resulting in exceedingly complicate infrageneric taxonomy. Extensive studies dealing with the variability of qualitative and quantitative traits, such as the shape and size of the rosette leaves, were not conducted in the past. This study aimed to analyse the extent of morphological variability of the rosette leaves regarding their shape and size and evaluate their potential to resolve taxonomic doubts. For this purpose, geometric morphometrics, as a tool for analysing minute shape variability, was used to discriminate mentioned species. The variability of the rosette leaves was statistically analysed within 15 populations using univariate (ANOVA) and multivariate statistical analysis (MANOVA, PCA, DA). Obtained results indicated highly statistically significant differences in rosette leaf shape and size between analysed complexes and species.