Natural Variation Research Articles

Systems biology approaches identify metabolic signatures of dietary lifespan and healthspan across species

Dietary restriction (DR) is a potent method to enhance lifespan and healthspan, but individual responses are influenced by genetic variations. Understanding how metabolism-related genetic differences impact longevity and healthspan are unclear. To investigate this, we used metabolites as markers to reveal how different genotypes respond to diet to influence longevity and healthspan traits. We analyzed data from Drosophila Genetic Reference Panel (DGRP) strains raised under AL and DR conditions, combining metabolomic, phenotypic, and genome-wide information. We employed two computational and complementary methods across species—random forest modeling within the DGRP as our primary analysis and Mendelian randomization in human cohorts as a secondary analysis. We pinpointed key traits with cross-species relevance as well as underlying heterogeneity and pleiotropy that influence lifespan and healthspan. Notably, orotate was linked to parental age at death in humans and blocked the DR lifespan extension in flies, while threonine supplementation extended lifespan, in a strain- and sex-specific manner. Thus, utilizing natural genetic variation data from flies and humans, we employed a systems biology approach to elucidate potential therapeutic pathways and metabolomic targets for diet-dependent changes in lifespan and healthspan.

Genome-wide association study uncovers pea candidate genes and metabolic pathways involved in rust resistance.

Pea (Pisum sativum L.) is an important temperate legume crop providing plant-based proteins for food and feed worldwide. Pea yield can be limited by several biotic stresses, among which rust represents a major limiting factor in many temperate and subtropical regions. Some efforts have been made to assess the natural variation in pea resistance to rust, but its efficient exploitation in breeding is limited since the resistance loci identified so far are scarce and their responsible gene(s) unknown. To overcome this knowledge gap, a comprehensive genome-wide association study (GWAS) has been performed on pea rust, caused by Uromyces pisi, to uncover genetic loci associated with resistance. Utilizing a diverse collection of 320 pea accessions, we evaluated phenotypic responses to two rust isolates using both traditional methods and advanced image-based phenotyping. We detected 95 significant trait-marker associations using a set of 26,045 Diversity Arrays Technology-sequencing polymorphic markers. Our in silico analysis identified 62 candidate genes putatively involved in rust resistance, grouped into different functional categories such as gene expression regulation, vesicle trafficking, cell wall biosynthesis, and hormonal signaling. This research highlights the potential of GWAS to identify molecular markers associated with resistance and candidate genes against pea rust, offering new targets for precision breeding. By integrating our findings into current breeding programs, we can facilitate the development of pea varieties with improved resistance to rust, contributing to sustainable agricultural practices and food security. This study sets the stage for future functional genomic analyses and the application of genomic selection approaches to enhance disease resistance in peas.

Food insecurity, associated climate factors and intervention mechanisms: a theoretical and empirical analysis of Ethiopia’s case

Purpose In our world, nearly nine million people die every year from hunger, losing one person to hunger every few seconds. Ethiopia is among the leading countries in the total number of people facing hunger. Several actions have been taken to achieve food security globally and at the household level. However, the problem of food insecurity is still high in many parts of the world. Why are we failing to achieve food security? And where should we start? This study aims to answer these main questions. Design/methodology/approach Relevant quantitative and qualitative data were duly used to describe food insecurity and associated climate factors globally and nationally. For this, PRISMA review methodology was used to help the systematic review. More than 90 relevant empirical and theoretical literature in the field were reviewed in an integrated way with practical evidence from Ethiopia. Governmental technical reports, NGOs reviews and other relevant empirical data referring to Ethiopia are thematically analyzed. Findings The subjectivity, dynamism and complexity of the concept of food security are found to be some of the issues challenging the practice of achieving food security. The unresolved issues in the combination and interaction of the four pillars of food security (food availability, access to food, food utilization and stability) are affecting its measurement. In this study, it is emphasized that food insecurity in rural and urban areas should be treated differently, as food insecurity in rural areas is directly related to food production which depends on rainfall patterns. In rural Ethiopia, rainfall variability was among the main causes of food insecurity. More importantly, it is indicated that rainfall variability does not affect all pillars of food security equally; its effect is more pronounced on food availability and stability aspects. Deconstructing the concept of food security to make it more pragmatic, and understanding the factors behind the rainfall variability should be the starting tasks in achieving food security. Further, even though food aid is preferred to react to transitory food insecurity, focusing on long-lasting preventive measures that address the root causes of the recurring food insecurity in rural areas of Ethiopia would be an effective way of addressing the problem. Practical implications Policy makers and other governmental and humanitarian agencies working on food security can make an evidence-based decision, shape policies and programs if they have clear information about the features of food insecurity, the nature of rainfall variability and critiques of the reacting mechanisms to food insecurity. Shortages of food in some place and food price crises in the other places have been both referring to food insecurity. This makes the understanding of food security situations more difficult to explain and communicate. Originality/value This study primarily clarifies the conceptual complexity surrounding food security as it currently exists. Further, it provides a comprehensive and quantitative description of the state of global to national food security, along with the associated variability of rainfall patterns that are related to it. It also provides a brief overview of the intervention mechanisms for addressing food security, with a specific focus on Ethiopia. This study has set a clear demarcation to assess food security in rural and urban areas.

Intelligent Manufacturing in Wine Barrel Production: Deep Learning-Based Wood Stave Classification

Innovative wood inspection technology is crucial in various industries, especially for determining wood quality by counting rings in each stave, a key factor in wine barrel production. (1) Background: Traditionally, human inspectors visually evaluate staves, compensating for natural variations and characteristics like dirt and saw-induced aberrations. These variations pose significant challenges for automatic inspection systems. Several techniques using classical image processing and deep learning have been developed to detect tree-ring boundaries, but they often struggle with woods exhibiting heterogeneity and texture irregularities. (2) Methods: This study proposes a hybrid approach combining classical computer vision techniques for preprocessing with deep learning algorithms for classification, designed for continuous automated processing. To enhance performance and accuracy, we employ a data augmentation strategy using cropping techniques to address intra-class variability in individual staves. (3) Results: Our approach significantly improves accuracy and reliability in classifying wood with irregular textures and heterogeneity. The use of explainable AI and model calibration offers a deeper understanding of the model’s decision-making process, ensuring robustness and transparency, and setting confidence thresholds for outputs. (4) Conclusions: The proposed system enhances the performance of automatic wood inspection technologies, providing a robust solution for industries requiring precise wood quality assessment, particularly in wine barrel production.

A chloroplast localized heavy metal-associated domain containing protein regulates grain calcium accumulation in rice

Calcium (Ca) is an essential mineral nutrient and plays a crucial signaling role in all living organisms. Increasing Ca content in staple foods such as rice is vital for improving Ca nutrition of humans. Here we map a quantitative trait locus that controls Ca concentration in rice grains and identify the causal gene as GCSC1 (Grain Ca and Sr Concentrations 1), which encodes a chloroplast vesicle localized homo-oligomeric protein. GCSC1 exhibits Ca2+ transport activity in heterologous assays in yeast and Xenopus laevis oocytes and is involved in the efflux of Ca2+ from the chloroplast to the cytosol. Knockout of GCSC1 results in increased chloroplast Ca concentration, lower stomatal conductance in leaves and enhanced Ca allocation to grains. Natural variation in grain Ca concentration is attributed to the variable expression of GCSC1 resulting from its promoter sequence variation. Our study identifies a chloroplast localized heavy metal-associated domain containing protein that regulates chloroplast Ca2+ efflux and provides a way to biofortify Ca in rice to benefit human nutrition.

Scale‐Dependent Drivers of Air‐Sea CO2 Flux Variability

AbstractIn climate studies, it is crucial to distinguish between changes caused by natural variability and those resulting from external forcing. Here we use a suite of numerical experiments based on the ECCO‐Darwin ocean biogeochemistry model to separate the impact of the atmospheric carbon dioxide (CO2) growth rate and climate on the ocean carbon sink — with a goal of disentangling the space‐time variability of the dominant drivers. When globally integrated, the variable atmospheric growth rate and climate exhibit similar magnitude impacts on ocean carbon uptake. At local scales, interannual variability in air‐sea CO2 flux is dominated by climate. The implications of our study for real‐world ocean observing systems are clear: in order to detect future changes in the ocean sink due to slowing atmospheric CO2 growth rates, better observing systems and constraints on climate‐driven ocean variability are required.

Depreciation and Trade Balance: An Exploration of the J-Curve Phenomenon in Indonesia

Research Originality: Global economic uncertainty has increased in recent years, leading to an appreciation of the US dollar relative to most global currencies, including the Indonesian rupiah. The novelty of this study is the inclusion of natural resource commodity price index variables, given the importance of these commodities in Indonesia's balance of trade.Research Objectives: This study aims to examine the effect of rupiah exchange rate depreciation on Indonesia's trade balance.Research Methods: This study uses a Vector Error Correction Model (VECM) to analyze Indonesia's balance of trade from January 2015 to December 2023, examining the potential J-Curve phenomenon resulting from the depreciation of the Indonesian rupiah.Empirical Result: Findings from the impulse response analysis suggest that the J-Curve phenomenon is present in Indonesia's balance of trade as a result of the rupiah's depreciation. The results of this study also confirm the positive effect of increasing natural resource commodity prices on Indonesia's trade balance.Implications: The implication of these findings is that exchange rate depreciation can improve Indonesia's trade balance only when natural resource commodity prices are rising. Conversely, if commodity prices decline, maintaining the stability of the rupiah exchange rate becomes crucial.JEL Classification: F11, F14, F31, Q02

Research on a coal seam modeling construction method based on improved kriging interpolation

To address the issues of large anomalous triangulations, invalid interpolations, and uneven boundary interpolations in kriging interpolation, we propose research on a coal seam modeling construction method based on improved kriging interpolation. The work methodology assumes that by introducing kriging interpolation and analyzing its problems, we improve the interpolation method via a local interpolation algorithm for large anomalous triangulations, an optimization algorithm for locally redundant interpolation points, and a nonuniform boundary adaptive local interpolation algorithm. These improvements allow the interpolation method to better reflect the variability and realistic nature of coal seams. The research results indicate that applying this method to the construction of the Dananhu No. 2 open-pit mine coal seam model has improved the issue of coal seam transition stiffness, such as abnormal large-area triangulation in areas with significant elevation differences. This approach appropriately reduces the memory space usage without altering the coal seam morphology (which saves approximately 27,000 KB of memory, equivalent to the space occupied by 4 out of 21 coal seams). It has also prevented inaccuracies in boundary line positioning and transitions caused by too low a density of points on the coal seam reserve boundary line, resulting in smoother model transitions at the boundaries that better align with the actual coal seam change trends, the error rate in coal quality estimation decreased by 62.69%. This study provides data support for mining planning and reduces costs. This method can be extended to the construction of all mine models.

Revolutionizing livestock sustainability: Pioneering breeding strategies for superior forage biomass and quality

Livestock primarily rely on forage crops as a source of feed and nutrition. The milk productivity of a cow or meat production in goat/sheep could directly be associated with the availability of a sufficient quantity of quality green fodders with essential nutrients in a balanced ratio. Feeding the cereal/grass: legume fodders in the required proportion will not only improve productivity but also the reproductive capacity of animals. However, many countries of the world experience a huge gap between demand and availability of green fodder. In this context, emphasis should be placed on developing efficient forage genotypes with increased biomass and quality as per the requirements of animals, duly considering their digestibility. Breeding approaches encompassing required classical approaches, including wide hybridization to exploit natural genetic variability, biotechnological tools such as transgenic technology, marker-assisted selection, genomic selection, and various omics techniques alongside high-throughput phenotyping using multispectral cameras, would help to sustain livestock productivity by meeting out the present and future fodder requirements coupled with enhanced nutrients

Scenario Generation Based on Ant Colony Optimization for Modelling Stochastic Variables in Power Systems

Uncertainty is an important subject in optimization problems due to the unpredictable nature of real variables in the power system area, which can condition the solution’s accuracy. The effective modelling of stochastic variables can contribute to the reduction in losses in the system under evaluation and facilitate the implementation of an effective response in advance. To model uncertainty variables, the most extended technique is the scenario generation (SG) method. This method evaluates possible combinations of complete curves. Classical scenario generation methods are founded on probability distributions or robust stochastic optimization. This paper proposes a novel approach for constructing scenarios using the Ant Colony Optimization (ACO) algorithm, referred to as ACO-SG. This methodology does not require a previous statistical study of uncertainty data to generate new scenarios. A historical dataset and the desired number of scenarios are the inputs inserted into the algorithm. In the case study, the algorithm used historical data from the Savona Campus Smart Polygeneration Microgrid of the University of Genoa. The approach was applied to generate scenarios of photovoltaic generation and building consumption. The low values of the Euclidean distance were used in order to check the validity of the scenarios. Moreover, the error deviation of the scenarios generated with the goal of daily power were 1.77% and 0.144% for the cases of PV generation and building consumption, respectively. The different results for both cases are explained by the characteristics of the specific cases. Despite these different results, both were significantly low, which indicates the capability of the algorithm to generate any kind of feature within curves and its adaptability to any case of SG.

A replicate crossover trial on the inter-individual variability of sleep indices in response to acute exercise undertaken by healthy men.

Using the necessary replicate-crossover design, we investigated whether there is inter-individual variability in home-assessed sleep in response to acute exercise. Eighteen healthy men (mean(SD): 26(6) years) completed two identical control (8-h laboratory rest, 08:45-16:45) and two identical exercise (7-h laboratory rest; 1-h laboratory treadmill run [62(7)% peak oxygen uptake], 15:15-16:15) trials in randomised sequences. Wrist-worn actigraphy (MotionWatch 8) measured home-based sleep (total sleep time, actual wake time, sleep latency, sleep efficiency) two nights before (nights 1-2) and three nights after (nights 3-5) the exercise/control day. Pearson's correlation coefficients quantified the consistency of individual differences between the replicates of control-adjusted exercise responses to explore: (1) immediate (night 3 minus night 2); (2) delayed (night 5 minus night 2); and (3) overall (average post-intervention minus average pre-intervention) exercise-related effects. Within-participant linear mixed models and a random-effects between-participant meta-analysis estimated participant-by-trial response heterogeneity. For all comparisons and sleep outcomes, the between-replicate correlations were non-significant, ranging from trivial-to-moderate (r range = -0.44 to 0.41, P≥0.065). Participant-by-trial interactions were trivial. Individual differences SDs were small, prone to uncertainty around the estimates indicated by wide 95% confidence intervals and did not provide support for true individual response heterogeneity. Meta-analyses of the between-participant, replicate-averaged condition effect revealed that, again, heterogeneity (τ) was negligible for most sleep outcomes. Control-adjusted sleep in response to acute exercise was inconsistent when measured on repeated occasions. Inter-individual differences in sleep in response to exercise were small compared to the natural (trial-to-trial) within-subject variability in sleep outcomes.

Exploring influential factors on biomass and diversity of ancient trees in human-dominated regions: A case study in Guangdong Province, China

Ancient trees, which are vital for biodiversity and cultural heritage, maintain species richness, ecosystem resilience, and societal interconnectedness. However, escalating global climate change and human activities have led to widespread declines in ancient tree diversity and habitat degradation. Hence, understanding ancient tree dynamics is vital for precise conservation strategies, fostering harmony between society and ecology. This study addresses the gap in understanding the factors influencing ancient tree biomass and diversity in human-dominated areas. This study first collected biomass equations for ancient tree species. The biomass of ancient trees within human-influenced regions of Guangdong Province was subsequently computed in detail. Finally, by employing a structural equation model, we sought to quantitatively elucidate the influence of natural variables (encompassing geography, topography, soil composition, and climatic conditions) alongside anthropogenic variables (including population density, per capita GDP, the human footprint, ancient villages, and religious sites) on both the biomass and species richness of ancient trees. The species with the highest total biomass included Ficus microcarpa L.f. (2.45 × 105 t), Ficus concinna (Miq.) Miq. (4.07 × 104 t), and Litchi chinensis Sonn. (3.16 × 104 t). Moreover, the biomass presented a pronounced phylogenetic signal and evolutionary conservation. According to the results of the structural equation model, across the entire study area, soil characteristics emerged as the most influential predictor among the natural factors (β = −0.14), whereas population density stood out as the foremost predictor among the anthropogenic factors (β = 0.23). Notably, both climatic conditions and anthropogenic factors contributed positively to an increase in ancient tree biomass (β = 0.02) (β = 0.07), albeit concurrently leading to a decrease in species richness (β = −0.05) (β = −0.08). In regions characterized by high levels of disturbance, anthropogenic factors indirectly increase biomass by increasing the abundance of large-diameter ancient trees (β = 0.28) while concurrently decreasing species richness (β = −0.11). Conversely, in areas with low levels of disturbance, anthropogenic influences also indirectly increase biomass (β = 0.19) by fostering the growth of large-diameter ancient trees while simultaneously promoting species richness (β = 0.01). This study investigated biomass and diversity factors in ancient trees within human-dominated regions of Guangdong Province. This study provides insights into ecosystem determinants, informing conservation strategies and contributing to biodiversity preservation amid societal and environmental dynamics.

The Use of Laser Sensing for Solving Meteorological Problems Related to Researching and Ensuring the Safety of Space Flights.

This paper is devoted to the issue of using laser (lidar) sensing to determine wind speed and direction when solving practical problems during the analysis of meteorological conditions in the area around spaceports. This issue is relevant both for making decisions on the possibility of a safe launch of a launch vehicle and for conducting search and rescue operations using groups of manned and unmanned aerial vehicles. Based on numerical and experimental modeling, it is shown that lidars provide highly accurate measurements of wind speed profiles and allow for the determination of weak wind shear in vertical and horizontal directions. This paper proposes a method for determining the main parameters of lidar sensing (range, resolution, detectability, etc.), which allows for the capabilities of this technology in solving the practical problems of meteorological monitoring to be predicted. Of particular interest in this article are experimental modeling data verifying the proposed calculation methods and the experimental determination of the capabilities of lidar diagnostics. This paper summarizes the data from multi-month experiments measuring wind speed in clear weather conditions when other means of remote diagnostics are ineffective. As a result of the experiments, a statistical distribution of the maximum range of wind speed measurement in normal weather conditions with natural variation in the concentration of scattering particles in the atmosphere was obtained. This article also discusses the possibility of combining lidars and meteorological radars for the meteorological support of flights.

Strong Linkage Between Observed Daily Precipitation Extremes and Anthropogenic Emissions Across the Contiguous United States

AbstractThe results of probabilistic event attribution studies depend on the choice of the extreme value statistics used in the analysis, particularly with the arbitrariness in the selection of appropriate thresholds to define extremes. We bypass this issue by using the Extended Generalized Pareto Distribution (ExtGPD), which jointly models low precipitation with a generalized Pareto distribution and extremes with a different Pareto tail, to conduct daily precipitation attribution across the contiguous United States (CONUS). We apply the ExtGPD to 12 general circulation models from the Coupled Model Intercomparison Project Phase 6 and compare counterfactual scenarios with and without anthropogenic emissions. Observed precipitation by the Climate Prediction Center is used for evaluating the GCMs. We find that greenhouse gases rather than natural variability can explain the observed magnitude of extreme daily precipitation, especially in the temperate regions. Our results highlight an unambiguous linkage of anthropogenic emissions to daily precipitation extremes across CONUS.

EQTL mapping in transgenic alpha-synuclein carrying Caenorhabditis elegans recombinant inbred lines.

Protein aggregation of α-synuclein (αS) is a genetic and neuropathological hallmark of Parkinson's disease (PD). Studies in the model nematode Caenorhabditis elegans suggested that variation of αS aggregation depends on the genetic background. However, which genes and genetic modifiers underlie individual differences in αS pathology remains unknown. To study the genotypic-phenotypic relationship of αS aggregation, we constructed a Recombinant Inbred Line (RIL) panel derived from a cross between genetically divergent strains C. elegans NL5901 and SCH4856, both harboring the human αS gene. As a first step to discover genetic modifiers 70 αS-RILs were measured for whole-genome gene expression and expression quantitative locus analysis (eQTL) were mapped. We detected multiple eQTL hot-spots, many of which were located on Chromosome V. To confirm a causal locus, we developed Introgression Lines (ILs) that contain SCH4856 introgressions on Chromosome V in an NL5901 background. We detected 74 genes with an interactive effect between αS and the genetic background, including the human p38 MAPK homologue pmk-1 that has previously been associated with PD. Together, we present a unique αS-RIL panel for defining effects of natural genetic variation on αS pathology, which contributes to finding genetic modifiers of PD.

Identification of Candidate Genes for Soybean Storability via GWAS and WGCNA Approaches

Soybean (Glycine max (L.) Merr.) is an important crop for both food and feed, playing a significant role in agricultural production and the human diet. During long-term storage, soybean seeds often exhibit reduced quality, decreased germination, and lower seedling vigor, ultimately leading to significant yield reductions in soybean crops. Seed storage tolerance is a complex quantitative trait controlled by multiple genes and is also influenced by environmental factors during seed formation, harvest, and storage. This study aimed to evaluate soybean germplasms for their storage tolerance, identify quantitative trait nucleotides (QTNs) associated with seed storage tolerance traits, and screen for candidate genes. The storage tolerance of 168 soybean germplasms was evaluated, and 23,156 high-quality single nucleotide polymorphism (SNP) markers were screened and analyzed through a genome-wide association study (GWAS). Ultimately, 14 QTNs were identified as being associated with seed storage tolerance and were distributed across the eight chromosomes of soybean, with five QTNs (rs25887810, rs27941858, rs33981296, rs44713950, and rs18610980) being newly reported loci in this study. In the linkage disequilibrium regions of these SNPs, 256 genes were identified. By combining GWAS and weighted gene co-expression network analysis (WGCNA), eight hub genes (Glyma.03G058300, Glyma.04G1921100, Glyma.04G192600, Glyma.04G192900, Glyma.07G002000, Glyma.08G329400, Glyma.16G074600, Glyma.16G091400) were jointly identified. Through the analysis of expression patterns, two candidate genes (Glyma.03G058300, Glyma.16G074600) potentially involved in seed storage tolerance were ultimately identified. Additionally, haplotype analysis revealed that natural variations in Glyma.03G058300 could affect seed storage tolerance. The findings of this research provide a theoretical foundation for understanding the regulatory mechanism underlying soybean storage.

Hidden Archaeological Remains in Heterogeneous Vegetation: A Crop Marks Study in Fortified Settlements of Northwestern Iberian Peninsula

This study evaluates the effectiveness of multispectral imaging via Unmanned Aerial Systems (UAS), in combination with advanced digital image processing techniques, for the detection and mapping of archaeological sites within diverse landscapes. The research focuses on six case studies located in the northwest of the Iberian Peninsula, a region marked by complex vegetation patterns and varying topography. The primary objective is to assess the potential of these non-invasive remote sensing techniques in identifying crop marks associated with buried structures from ancient, fortified settlements. By means of Principal Component Analysis (PCA) and vegetation indices, the study aims to pinpoint areas of interest that may indicate the presence of archaeological features, while effectively distinguishing them from modern disturbances or natural terrain variations. The research encountered several challenges, including seasonal variations in crop conditions and recent land-use changes. The methodology successfully identified distinct archaeological features. In some instances, natural vegetation variability, typically seen as an obstacle, enhanced the visibility of crop marks, aiding in the detection of underlying structures. These results offer a cost-effective and scalable option for preliminary archaeological surveys, particularly in refining survey methodologies and guiding future excavation efforts aimed at uncovering and preserving ancient, fortified settlements in the region.

Madden Julian Oscillation Moves Faster as the Meridional Moisture Gradient Intensifies in a Warming World

AbstractThe eastward‐moving large‐scale convective system associated with the Madden‐Julian Oscillation (MJO) significantly impact global weather and climate. Recent decades have seen notable changes in the MJO's lifecycle due to non‐uniform tropical ocean warming, with the roles of natural climate variability and anthropogenic influence still requiring quantification. This study examines observed and projected long‐term changes in the MJO phase speed using four twentieth‐century reanalyses and CMIP6 simulations. We find a substantial increase in MJO phase speed in three reanalyses during the twentieth century (0.6–1.2 m s⁻1 century⁻1) and further increase in MJO phase speed during the twenty‐first century (0.3–1.5 m s⁻1 century⁻1), with notable multidecadal fluctuations. We attribute the overall acceleration of the MJO to the global warming‐driven increase in the meridional moisture gradient around the warm pool while attributing the multidecadal variability in the MJO phase speed to changes in the zonal moisture gradient associated with the Pacific Decadal Oscillation.

Protein catabolites as blood-based biomarkers of aging physiology: Findings from the Dog Aging Project.

Our understanding of age-related physiology and metabolism has grown through the study of systems biology, including transcriptomics, single-cell analysis, proteomics and metabolomics. Studies in lab organisms in controlled environments, while powerful and complex, fall short of capturing the breadth of genetic and environmental variation in nature. Thus, there is now a major effort in geroscience to identify aging biomarkers and to develop aging interventions that might be applied across the diversity of humans and other free-living species. To meet this challenge, the Dog Aging Project (DAP) is designed to identify cross-sectional and longitudinal patterns of aging in complex systems, and how these are shaped by the diversity of genetic and environmental variation among companion dogs. Here we surveyed the plasma metabolome from the first year of sampling of the Precision Cohort of the DAP. By incorporating extensive metadata and whole genome sequencing information, we were able to overcome the limitations inherent in breed-based estimates of genetic and physiological effects, and to probe the physiological and dietary basis of the age-related metabolome. We identified a significant effect of age on approximately 40% of measured metabolites. Among other insights, we discovered a potentially novel biomarker of age in the post-translationally modified amino acids (ptmAAs). The ptmAAs, which can only be generated by protein hydrolysis, covaried both with age and with other biomarkers of amino acid metabolism, and in a way that was robust to diet. Clinical measures of kidney function mediated about half of the higher ptmAA levels in older dogs. This work identifies ptmAAs as robust indicators of age in dogs, and points to kidney function as a physiological mediator of age-associated variation in the plasma metabolome.

Negotiation Strategies and Their Effect on Organizational Success: Analytical Research in The Iraqi Ministry of Industry and Minerals

Purpose: This study aims to determine the impact of Negotiation Strategies (NS) on Organizational Success(OS) in the Iraqi Ministry of Industry and Minerals. Conceptual Framework: The NS includes dimensions (cooperative strategy, competitive strategy, avoidance strategy, bargaining strategy and absorption strategy) measured by (Thomas, 2008), and OS includes dimensions (resources, organizational structure, management, process and knowledge management) measured by (Hamedi & Azimi, 2013)​. Design/Methodology/approach: The descriptive analytical method was adopted in completing the current research, on a sample of senior and middle leaders in the Iraqi Ministry of Industry and Minerals under study, which numbered (120) individuals, and they were selected using the comprehensive survey stratified sample method. Findings: We found that NS plays a positive role in achieving organizational success in the Iraqi Ministry of Industry and Minerals. NS enhances cooperation and coordination between parties within the ministry, which has led to improved operations. Thus, the main and sub-hypotheses of the research were proven correct. Research, Practical & Social implications: Identify the latest developments and fundamental ideas related to the variables of the study (NS - organizational success) to find interrelationships between the variables. Originality/value: Providing those concerned and interested with practical and theoretical information that explains the nature of the research variables, their sources and causes, the extent of their reflection on the research sample, and the extent of the interest of the researched organization (the Iraqi Ministry of Industry and Minerals) in their dimensions.