Environment Interaction Effects Research Articles

Growth of Clones of Eucalyptus urophylla in Two Contrasting Soil Conditions in Plantations of Southeastern Mexico

Eucalyptus urophylla is important for the establishment of commercial forest plantations in Mexico. Genetic improvement programs are currently being implemented to increase timber productivity. The objectives of this study were to evaluate the adaptability and growth stability of 26 clonal lines of E. urophylla in Acrisol and Fluvisol soils and to identify the most suitable genotypes for each soil type. Tree survival, diameter at breast height, and total height were measured annually for six years. These variables were used to estimate individual volume, volume per hectare, and mean annual (MAIv) and current annual (CAIv) volume increment. Survival ranged from 14 to 100% in the Acrisol soil and from 0 to 89% in the Fluvisol soil. Volume per hectare ranged from 65.3 to 488.7 m3, MAIv from 11.1 to 83.1 m3 ha−1 year−1, and CAIv from 2.4 to 134.7 m3 ha−1 year−1. Individual heritability (Hi2) was moderate (0.29–0.49) while the mean heritability of the cloned lines was high (0.73–0.90), indicating that growth is subject to high genetic control. Diameter, height, and volume presented no genotype × environment interaction effects, demonstrating stability in the growth of the clonal lines in both soil types.

Genotype by environment interaction and performance stability of common bean (Phaseolus vulgaris L.) cultivars grown in Dawuro zone, Southwestern Ethiopia

Abstract The performance of most crop genotypes varies significantly depending on their genotype–environment interactions. Providing information on high-yielding and well-adapted common bean genotypes in a given environment is crucial for small-holder farmers to boost their productivity. A field trial was conducted at the Gendo and Wara locations over two consecutive seasons to understand genotype–environment interactions on grain yield and to identify and select common bean genotypes that were better adapted and had high yield potential for production. Ten genotypes were evaluated for their performance using a randomized complete block design with three replications. The AMMI and GGE models were used to identify superior genotypes regarding their mean grain yield performance. The AMMI analysis of variance results showed that grain yield was significantly (p ≤ 0.05) influenced by genotype–environment interactions. This shows that genotypes varied in grain yield mean performance across environments, indicating the possibility of selecting suitable genotypes with specific and broad adaptability. The sum of squares factor indicated that grain yield variation was contributed by the genotype (61.0%), followed by the environment (29.80%) and the genotype–environment interaction effect (9.20%) of the total variation. The AMMI and GGE biplot analysis results showed that SER-119 and SER-125 were identified as the best genotypes regarding their mean grain yield performance. Additionally, the genotype selection index analysis result indicated that SER-119 and SER-125 were stable and consistently exhibited high mean performance in all evaluated settings. Therefore, SER-119 and SER-125 are prime candidates for production in various agroecologies suitable for common bean cultivation in Dawuro zones. The use of tools like GGE biplots and AMMI analysis helped visualize the genotype–environment interactions, aiding in the selection of appropriate genotypes for both specific and wide adaptability.

Pod Yield Stability of Best Groundnut Varieties from National Agricultural Research Stations Program in West Africa

This study aimed to evaluate different varieties of groundnut introduced from four breeding programs in West Africa for their stability and adaptability in agro-ecological zones of Niger. The study used a randomized complete block design with three replicates at three locations. Analysis of variance revealed significant variation among varieties and the effect of environment on pod yield was highly significant (P<0.001). GGE biplot analysis was used to identify the best varieties for each environment and assess the stability of the newly introduced varieties. The GGE biplot described all the observed variations and explained 98.42% of the total variation, indicating a strong genotype by environment interaction effect. The evaluation of varietal performance indicated that Pyr-370 and Rafeet Car thrived exceptionally well in Bengou and Magaria, respectively, while El_Tarna exhibited the highest performance at Tarna. Variety SH470P was the most stable and productive among the introduced varieties in the different environments, while ICGV86124 was stable but low yielding. On the other hand, the introduced varieties ICGV86024, ICGV-IS14857 and Pyr-370 demonstrated superior productivity, yet their performance inconsistency underscored inherent instability.

Improving canopy transpiration model performance by considering concurrent hot and dry conditions

CONTEXTThe Jarvis-Stewart model has commonly been used to estimate transpiration. However, its assumption regarding independent effects from different influencing factors has seldom been explored. OBJECTIVEThe aim is to test whether the Jarvis-Stewart model can well estimate transpiration under concurrent hot and dry conditions with strong environmental interactions. A secondary objective was to test was to propose a new coupling method without the environmental-independence assumption for transpiration estimation. METHODSThis study tested and compared 24 Jarvis-Stewart models composed of various constraint functions to determine the optimal model structure for an orange orchard under concurrent hot and dry conditions. Meanwhile, a new coupling equation that considers interactive environmental effects on transpiration was proposed, and a total of 48 configurations were examined to determine the most effective configuration. A comprehensive comparison was made between the best Jarvis-Stewart model and the best coupling model based on a Bayesian framework. RESULTS AND CONCLUSIONSResults showed that with fewer parameters, the best new coupling model significantly improved model performance compared with the best Jarvis-Stewart model. Specifically, estimation accuracy was slightly improved, with the average mean relative error decreased from 11.79% to 11.06%, and the average coefficient of determination increased from 0.67 to 0.72. More importantly, estimation uncertainty was significantly decreased, with the average uncertainty band width reduction of 42%. Moreover, the new model generated some water use information that was much more consistent with measured data and did not suffer from over-fitting problems as the Jarvis-Stewart model suffered from. Furthermore, the new model did not require additional data or computational cost. SIGNIFICANCEThe results of our study emphasize the necessity for studying environmental interaction effects on plant water consumption, particularly under the possibility of concurrent hot and dry conditions that are likely to occur under future climate conditions.

Effects of virtual agents on interaction efficiency and environmental immersion in MR environments

BackgroundPhysical entity interactions in mixed reality (MR) environments aim to harness human capabilities in manipulating physical objects, thereby enhancing virtual environment (VEs) functionality. In MR, a common strategy is to use virtual agents as substitutes for physical entities, balancing interaction efficiency with environmental immersion. However, the impact of virtual agent size and form on interaction performance remains unclear. MethodsTwo experiments were conducted to explore how virtual agent size and form affect interaction performance, immersion, and preference in MR environments. The first experiment assessed five virtual agent sizes (25%, 50%, 75%, 100%, and 125% of physical size). The second experiment tested four types of frames (no frame, consistent frame, half frame, and surrounding frame) across all agent sizes. Participants, utilizing a headmounted display, performed tasks involving moving cups, typing words, and using a mouse. They completed questionnaires assessing aspects such as the virtual environment effects, interaction effects, collision concerns, and preferences. ResultsResults from the first experiment revealed that agents matching physical object size produced the best overall performance. The second experiment demonstrated that consistent framing notably enhances interaction accuracy and speed but reduces immersion. To balance efficiency and immersion, frameless agents matching physical object sizes were deemed optimal. ConclusionsVirtual agents matching physical entity sizes enhance user experience and interaction performance. Conversely, familiar frames from 2D interfaces detrimentally affect interaction and immersion in virtual spaces. This study provides valuable insights for the future development of MR systems.

Investigating of the Genotype × Environment Interaction Effect for Grain Yield in Red Bean Genotypes Using AMMI and GGE Biplot Methods

Investigating of the Genotype × Environment Interaction Effect for Grain Yield in Red Bean Genotypes Using AMMI and GGE Biplot Methods

Gene‒environment interaction effect of hypothalamic‒pituitary‒adrenal axis gene polymorphisms and job stress on the risk of sleep disturbances.

Studies have shown that chronic exposure to job stress may increase the risk of sleep disturbances and that hypothalamic‒pituitary‒adrenal (HPA) axis gene polymorphisms may play an important role in the psychopathologic mechanisms of sleep disturbances. However, the interactions among job stress, gene polymorphisms and sleep disturbances have not been examined from the perspective of the HPA axis. This study aimed to know whether job stress is a risk factor for sleep disturbances and to further explore the effect of the HPA axis gene × job stress interaction on sleep disturbances among railway workers. In this cross-sectional study, 671 participants (363 males and 308 females) from the China Railway Fuzhou Branch were included. Sleep disturbances were evaluated with the Pittsburgh Sleep Quality Index (PSQI), and job stress was measured with the Effort-Reward Imbalance scale (ERI). Generalized multivariate dimensionality reduction (GMDR) models were used to assess gene‒environment interactions. We found a significant positive correlation between job stress and sleep disturbances (P < 0.01). The FKBP5 rs1360780-T and rs4713916-A alleles and the CRHR1 rs110402-G allele were associated with increased sleep disturbance risk, with adjusted ORs (95% CIs) of 1.75 [1.38-2.22], 1.68 [1.30-2.18] and 1.43 [1.09-1.87], respectively. However, the FKBP5 rs9470080-T allele was a protective factor against sleep disturbances, with an OR (95% CI) of 0.65 [0.51-0.83]. GMDR analysis indicated that under job stress, individuals with the FKBP5 rs1368780-CT, rs4713916-GG, and rs9470080-CT genotypes and the CRHR1 rs110402-AA genotype had the greatest risk of sleep disturbances. Individuals carrying risk alleles who experience job stress may be at increased risk of sleep disturbances. These findings may provide new insights into stress-related sleep disturbances in occupational populations.

Enhancing Cowpea Tolerance to Elevated Temperature: Achievements, Challenges and Future Directions

Despite its ability to thrive in high-temperature environments, cowpea productivity can be hampered by heat stress, particularly when night air temperatures exceed 17 °C. The crop’s germplasm pool potentially possesses significant genetic variability that can be harnessed to breed for heat-tolerant varieties. Progress in improving the crop for heat tolerance has been limited, especially under the hot, short-day environments typical of sub-Saharan Africa. Only a few heat-tolerant varieties have been released, partly due to the limited understanding of heat stress tolerance mechanisms and environmental interaction effects on genotypes, as well as imprecise phenotyping. This review contributes to the literature on cowpea heat stress by highlighting key achievements, challenges, and future directions in breeding heat-tolerant cowpea genotypes and by providing additional information from the recent literature. We opine that the genetic variability for heat tolerance-related traits in cowpea has not been sufficiently exploited in developing varieties adapted to the target production environments. Therefore, attention should be given to assessing the crop’s genetic repository by targeting adaptive, morphological, and physiological traits that enhance heat stress tolerance. We propose that breeding programs integrate phenotyping of whole-plant physiological traits and molecular breeding to identify breeder-friendly markers for routine selection. This should be followed by introgression of the heat-tolerant favourable alleles to adapted susceptible varieties using rapid and precise approaches that take advantage of modern genetic and genomic resources such as innovative genetic resources, genomic selection, speed breeding, and genome editing technologies. These tools hold great promise in fast-tracking the development of improved heat-tolerant varieties and incorporating the must-have traits preferred by cowpea farmers and consumers. In view of the likely increase in atmospheric temperature to be occasioned by climate change, there is an urgent need to develop heat-tolerant cowpea varieties to ensure the sustainability of current and future cropping and agri-food systems.

Functional and Compositional Changes in Sirex noctilio Gut Microbiome in Different Habitats: Unraveling the Complexity of Invasive Adaptation.

The mutualistic symbiosis relationship between the gut microbiome and their insect hosts has attracted much scientific attention. The native woodwasp, Sirex nitobei, and the invasive European woodwasp, Sirex noctilio, are two pests that infest pines in northeastern China. Following its encounter with the native species, however, there is a lack of research on whether the gut microbiome of S. noctilio changed, what causes contributed to these alterations, and whether these changes were more conducive to invasive colonization. We used high-throughput and metatranscriptomic sequencing to investigate S. noctilio larval gut and frass from four sites where only S. noctilio and both two Sirex species and investigated the effects of environmental factors, biological interactions, and ecological processes on S. noctilio gut microbial community assembly. Amplicon sequencing of two Sirex species revealed differential patterns of bacterial and fungal composition and functional prediction. S. noctilio larval gut bacterial and fungal diversity was essentially higher in coexistence sites than in separate existence sites, and most of the larval gut bacterial and fungal community functional predictions were significantly different as well. Moreover, temperature and precipitation positively correlate with most of the highly abundant bacterial and fungal genera. Source-tracking analysis showed that S. noctilio larvae at coexistence sites remain dependent on adult gut transmission (vertical transmission) or recruitment to frass (horizontal transmission). Meanwhile, stochastic processes of drift and dispersal limitation also have important impacts on the assembly of S. noctilio larval gut microbiome, especially at coexistence sites. In summary, our results reveal the potential role of changes in S. noctilio larval gut microbiome in the successful colonization and better adaptation of the environment.

Enhanced polygenic risk score incorporating gene-environment interaction suggests the association of major depressive disorder with cardiac and lung function.

Depression has been linked to an increased risk of cardiovascular and respiratory diseases; however, its impact on cardiac and lung function remains unclear, especially when accounting for potential gene-environment interactions. We developed a novel polygenic and gene-environment interaction risk score (PGIRS) integrating the major genetic effect and gene-environment interaction effect of depression-associated loci. The single nucleotide polymorphisms (SNPs) demonstrating major genetic effect or environmental interaction effect were obtained from genome-wide SNP association and SNP-environment interaction analyses of depression. We then calculated the depression PGIRS for non-depressed individuals, using smoking and alcohol consumption as environmental factors. Using linear regression analysis, we assessed the associations of PGIRS and conventional polygenic risk score (PRS) with lung function (N = 42886) and cardiac function (N = 1791) in the subjects with or without exposing to smoking and alcohol drinking. We detected significant associations of depression PGIRS with cardiac and lung function, contrary to conventional depression PRS. Among smokers, forced vital capacity exhibited a negative association with PGIRS (β = -0.037, FDR = 1.00 × 10-8), contrasting with no significant association with PRS (β = -0.002, FDR = 0.943). In drinkers, we observed a positive association between cardiac index with PGIRS (β = 0.088, FDR = 0.010), whereas no such association was found with PRS (β = 0.040, FDR = 0.265). Notably, in individuals who both smoked and drank, forced expiratory volume in 1-second demonstrated a negative association with PGIRS (β = -0.042, FDR = 6.30 × 10-9), but not with PRS (β = -0.003, FDR = 0.857). Our findings underscore the profound impact of depression on cardiac and lung function, highlighting the enhanced efficacy of considering gene-environment interactions in PRS-based studies.

Variation in seed protein, selected minerals, phytic acid, and potential mineral bioavailability of cowpea [Vigna unguiculata (L.) Walp] mutants and accessions

AbstractCowpea [Vigna unguiculata (L.) Walp] has the potential to contribute to the eradication of hunger and malnutrition. The study objectives were to evaluate the variability of cowpea mutants; International Institute of Tropical Agriculture (IITA) and South African genotypes for protein content, selected minerals, phytic acid, and the bioavailability of iron (Fe) and zinc (Zn); to identify superior cowpea mutants, IITA, and South African genotypes; and to determine the correlation between measured characteristics. A total of 31 cowpea genotypes were planted at five different environments in South Africa. Significant (p ≤ 0.05) genotype and genotype by environment interaction effects were observed for protein, minerals, and phytic acid concentration. High broad‐sense heritability (&gt;50%) was observed for almost all the characteristics. Thirteen Namibian mutants, five IITA genotypes, and six South African genotypes that combined high protein, Fe, and Zn were identified. The IITA genotype IT93K‐452‐1 and South African genotypes Oloyin and Orelu had good Fe bioavailability. Nine Namibian mutants, four IITA genotypes, and three South African genotypes were associated with high values for protein, manganese, phytic acid, molar ratios of phytic acid with Fe and Zn, and ash. The identified superior cowpea mutants, IITA and South African genotypes could be a good source of the essential minerals and should be further studied for adaptability in South African agroecologies.

Genotype by environment (G × E) interaction analysis for seed yield and other contributing traits in linseed (Linum usitatissimum L.) across conventional and zero budget natural farming production systems in north-western Himalayas

Development of high-yielding and stable linseed (Linum usitatissimum L.) genotypes in Himachal Pradesh under zero budget naturalfarming system (ZBNF) is an absolute need. A study was conducted to evaluate 30 elite genotypes of linseed for yield and stability across sixteen different environments over two years using Additive main effects and a multiplicative interaction model. For seed yield the environment, genotype and genotype x environment interaction (GEI) effects were highly significant (p &lt;0.001), with contributions to total observed variation of 89.74, 0.81 and 8.08%, respectively. Out of the two production systems, ZBNF was observed to show consistently poor mean yields as compared to the conventional system across all locations. However, locations Kangra and Dhaulakaun performed better under the ZBNF system than Palampur and Bajaura. ZBNF was also identified as less discriminating with weak interactive forces. As per Eberhart and Russell model, the most stable and high-yielding genotype was Surbhi whereas, as per AMMI model, the ASV and GSI values indicated Giza-7 (G18), KL-285 (G16), KL-311 (G1) and Surbhi (G28) as stable and high in grain yield. For yield attributing traits such as primary branches per plant genotypes Janki (G27) and KL-314 (G4), for secondary branches per plant genotypes KL-263 (G10) and Janki (G27), for seeds per capsule genotypes KL-279 (G13), Binwa (G30) and Him Alsi-2 (G19), for trait biological yield per plant Jeewan (G22), for harvest index KL-236 (G6) and Him Alsi-1 (G26) and for 1000 seed weight genotypes Him Alsi-1 (G26) and KL-285 (G16) showed, high stability along with high mean performance and therefore could be selected. Genotype KL-284 (G15) showed specific adaptation under ZBNF system of Bajaura and is therefore recommended for production under respective environments after further evaluation

CLIMATE CHANGE IMPACT ON CHLOROPHYLL CONTENT AND GRAIN YIELD OF BREAD WHEAT (TRITICUM AESTIVUM L.)

The paper presents the study of bread wheat's chlorophyll content and grain yield traits under changing environmental conditions — optimal water supply and simulated water-deficit conditions. Selecting 15 wheat cultivars from different regions (Russia, Kazakhstan, Kyrgyzstan, Tajikistan, Turkey, and CIMMYT) based on economically valuable characteristics became the specimens for the presented study that evaluated the SPAD indicators in correlation with the grain yield in bread wheat and determined the vital role of genotypes, environments, and genotype by environment interaction effects. The relationship between chlorophyll content and yield parameters under different growing conditions was also well-defined. The results revealed that ecosystems had more influence on the chlorophyll content than the wheat genotypes. Several wheat cultivars with soil moisture tolerance have gained identification, along with the correlation coefficient between chlorophyll content and grain yield under varied environmental conditions of water supply. The study validated the role of genotypes and environments in the manifestation of responses to stress conditions.

ADDITIVE MAIN EFFECT AND MULTIPLICATIVE INTERACTION ANALYSIS FOR PRODUCTION TRAITS IN RICE RECOMBINANT INBRED LINES USING AMMI BIPLOT TECHNIQUE

The experiment was performed to assess the performance of 87 rice recombinant inbred lines (RILs), identify&#x0D; the productive environments and quantify genotype × environment interaction (GEI) effects on yield and yield&#x0D; related traits across four locations of the Khyber Pakhtunkhwa Province in Pakistan between 2021 and 2022.&#x0D; The pooled ANOVA showed significant differences of GEI for all traits. Across the studied environments, RIL&#x0D; AUP-3 took minimum days to heading (139) and produced the highest grain yield (4.1 t ha-1). AUP-3 and AUP30 displayed the highest kernel number in panicle of 214 and 226, respectively. Based on AMMI1 and AMMI2&#x0D; biplots, RIL AUP-3 was unanimously declared as the most stable and the highest yielding genotype. AMMI&#x0D; biplot analysis cumulatively explained 55.3%, 46.3% and 52.1% of total variation due to GEI for days to&#x0D; maturity, grains panicle-1 and grain yield, respectively. The AMMI biplot analysis confirmed the differential&#x0D; response of genotypes across environments, suggesting environment-based expression of genes. The AMMI&#x0D; biplot also manifested AUP-3 and AUP-30 as ideal genotypes for grains panicle-1 and grain yield, respectively.&#x0D; The Peshawar (E-1 and E-5) and Mingora (E-2) were the most discriminating and representative environment&#x0D; for grains panicle-1 and grain yield. Application of various stability models in this study identified AUP-3 and&#x0D; AUP-30 as the most stable and widely adapted genotype for grain yield and its components. The AMMI analysis&#x0D; identified genotypes both for specific and wide adaptation. Based on stability and yield performance, said&#x0D; genotypes were identified with superior performance than check cultivars. Therefore, recommended for&#x0D; commercialization. Also environments were clustered regardless of their geographical location, which revealed&#x0D; unpredictable nature of agro-climatic conditions across four tested locations.

Identification of QTLs for plant height and branching related traits in cultivated peanut

Plant height (PH), primary lateral branch length (PBL) and branch number (BN) are architectural components impacting peanut pod yield, biomass production and adaptivity to mechanical harvesting. In this study, a recombinant inbred population consisting of 181 individual lines was used to determine genetic controls of PH, PBL and BN across three environments. Phenotypic data collected from the population demonstrated continuous distributions and transgressive segregation patterns. Broad-sense heritability of PH, PBL and BN was found to be 0.87, 0.88 and 0.92, respectively. Unconditional individual environmental analysis revealed 35 additive QTLs with phenotypic variation explained (PVE) ranging from 4.57 to 21.68%. A two-round meta-analysis resulted in 24 consensus and 17 unique QTLs. Five unique QTLs exhibited pleiotropic effects and their genetic bases (pleiotropy or tight linkage) were evaluated. Joint analysis was performed to estimate the QTL by environment interaction (QEI) effects on PH, PBL and BN, which collectively explained phenotypic variations of 10.80, 11.02, and 7.89%, respectively. We identified 3 major and stable QTL regions (uq9-3, uq10-2 and uq16-1) on chromosomes 9, 10 and 16, spanning 1.43-1.53 Mb genomic regions. Candidate genes involved in phytohormones biosynthesis, signaling and cell wall development were proposed to regulate these morphological traits. These results provide valuable information for further genetic studies and development of molecular markers applicable for peanut architecture improvement.

A simulation-based assessment of the efficiency of QTL mapping under environment and genotype x environment interaction effects.

The objective of this simulation-based study was to assess how genes, environments, and genotype x environment (GxE) interaction affect the quantitative trait loci (QTL) mapping efficiency. The simulation software performed 50 samplings of 300 recombinant inbred lines (RILs) from a F2, which were assessed in six environments. The RILs were genotyped for 977 single nucleotide polymorphisms (SNP) and phenotyped for grain yield. The average SNP density was 2 cM. We defined six QTLs and 190 minor genes. The trait heritability ranged from 30 to 80%. We fitted the single QTL model and the multiple QTL model on multiple phenotypes. The environment and complex GxE interaction effects led to a low correlation between the QTL heritability and power. The single- and across-environment analyses allowed all QTLs be declared, with an average power of 28 to 100%. In the across-environment analysis, five QTLs showed average power in the range 46 to 82%. Both models provided a good control of the false positive rate (6%, on average) and a precise localization of the QTLs (bias of 2 cM, on average). The QTL power in each environment has a high positive correlation with the range between QTL genotypes for the sum of the additive, environment, and GxE interaction effects (0.76 to 0.96). The uncertainty about the magnitude and sign of the environment and GxE interaction effects makes QTL mapping in multi-environment trials unpredictable. Unfortunately, this uncertainty has no solution because the geneticist has no control over the magnitude and sign of the environment and GxE interaction effects. However, the single- and across-environment analyses are efficient even under a low correlation between QTL heritability and power.

Tracking the source of antibiotic resistome in the stormwater network drainage in the presence of sewage illicit connections

Stormwater pipes are illicitly connected with sewage in many countries, which means that sewage enters stormwater pipes and the drainage is discharged to surface water without any treatment. Sewage contains more pathogens and highly risky antibiotic resistance genes (ARGs) than surface runoff. Therefore, sewage may alter the microbial and ARG compositions in stormwater pipe drainage, which in turn leads to an increased risk of resistance in surface water. However, the effects of sewage on ARGs in the drainage of stormwater networks have not been systematically studied. This study characterized the microbial and ARG composition of several environmental compartments of a typical stormwater network and quantified their contributions to those in the drainage. This network transported ARGs and microorganisms from sewage, sediments in stormwater pipes, and surface runoff into the drainage and thus into the river. According to metagenomic analysis, multidrug resistance genes were most abundant in all samples and the numbers and relative abundance of ARGs in the drainage collected during wet weather were comparable to that of sewage. The results of SourceTracker showed that the relative contribution of sewage was double that of rainwater and surface runoff in the drainage during wet weather for both microorganisms and ARGs. Desulfovibrio, Azoarcus, and Sulfuritalea were connected with the greatest number of ARGs and were most abundant in the sediments of stormwater pipes. Furthermore, stochastic processes were found to dominate ARG and microbial assembly, as the effects of high hydrodynamic intensity outweighed the effects of environmental filtration and species interactions. The findings of this study can increase our understanding of ARGs in stormwater pipe drainage, a crucial medium linking ARGs in sewage to environmental ARGs.

Synthesis of an autochthonous microbial community by analyzing the core microorganisms responsible for the critical flavor of bran vinegar

Traditional bran vinegar brewing unfolds through natural fermentation, a process driven by spontaneous microbial activity. The unique metabolic activities of various microorganisms lead to distinct flavors and qualities in each batch of vinegar, making it challenging to consistently achieve the desired characteristic flavor compounds. Therefore, identifying the critical microbial species responsible for flavor production and designing starter cultures with improved fermentation efficiency and characteristic flavors are effective methods to address this discrepancy. In this study, 11 core functional microbial species affecting the fermentation flavor of Sichuan shai vinegar (Cupei were placed outside solarization and night-dew for more than one year, and vinegar was the liquid leached from Cupei) (SSV), were revealed by combining PacBio full-length diversity sequencing based on previous metagenomics. The effects of environmental factors and microbial interactions on the growth of 11 microorganisms during fermentation were verified using fermentation experiments. Ultimately, the microbial community was strategically synthesized using a 'top-down' approach, successfully replicating the distinctive flavor profile of Sichuan shai vinegar (SSV). The results showed that the interaction between microorganisms and environmental factors affected microorganism growth. Compared with traditional fermentation, the synthetic microbial community’s vinegar-fermented grains (Cupei) can reproduce the key flavor of SSV and is conducive to the production of amino acids. In this study, the key flavor of SSV was reproduced through rational design of the synthetic microbial community. This achievement holds profound significance for the broader application of microbiome assembly strategies in the realm of fermented foods.

Effects of environmental and genetic interactions on job burnout in coal miners: interactions between occupational stress, coping styles, and NR3C2 gene polymorphisms.

To investigate the current situation regarding occupational burnout among coal miners, explore the relationship between NR3C2 gene polymorphism and occupational burnout, and analyze the influence of the interaction between environment and gene on occupational burnout. This study provides a scientific basis for formulating health strategies to combat job burnout. A total of 1,500 first-line coal mine workers were selected by cluster random sampling, and the job burnout scale, job content questionnaire (JCQ), and simplified coping style questionnaire (SCSQ) were used for the questionnaire survey. A total of 150 workers were randomly selected from the high burnout group and the low burnout group, and a total of 300 workers were selected as the research objects to examine the relationship between gene polymorphism, environment-gene interactions and burnout. This study employed iMLDRTM genotyping technology for NR3C2 gene (rs5522, rs2070950) polymorphism analysis. The relationship between the occurrence of job burnout, occupational stress, coping styles and the NR3C2 gene was analyzed. Finally, a total of 1,282 valid questionnaires were retrieved, with an effective recovery rate of 85.5%. The study included 128 participants (10%) with zero burnout, 400 (31.2%) with mild burnout, 649 (50.6%) with moderate burnout and 105 (8.2%) with severe burnout. There were significant differences in the rate of burnout among miners with respect to sex, age, working years, educational level, shifts, and marital status (P < 0.05). The difference in occupational stress between the different job burnout groups was statistically significant (P < 0.05). Compared with the GG genotype of rs2070950 of the NR3C2 gene, the CC genotype was identified as a susceptibility gene for occupational burnout (P < 0.05). In respect to rs5522, rs2070950, occupational stress, positive coping, and negative coping, the low-risk group was unlikely to suffer from job burnout compared with the high-risk group (OR = 0.103, 95%CI: 0.058-0.182). In addition to demographic characteristics, occupational stress and negative coping styles were also identified as risk factors for job burnout. The interaction between locus rs5522, locus rs2070950, occupational stress, positive response, and negative response were found to affect the incidence of occupational burnout.

Genotype by environment interaction for agro-morphological traits and herbage nutritive values and fodder yield stability in oat (Avena sativa L.) using AMMI analysis in Ethiopia

This study aimed to evaluate the genotype by environment interaction for agro-morphological traits and fodder nutritive values of oat (Avena sativa L.) genotypes under diverse environmental conditions and to identify high fodder yielding and consistently performing genotypes in Ethiopia. Oat genotypes usually demonstrate inconsistent performance in different environments due to variations in growing environments and the existence of significant genotype by environment interaction complicates the selection process. Therefore, the genotype by environment interaction study was executed using the additive main effects and multiplicative interactions model to select superior and stable genotypes. The effects of genotype by environment interaction on fodder yield stability in twenty-four oat genotypes were investigated in nine environments using a randomized complete block design. The additive main effects and multiplicative interaction analysis of variance for fodder yield showed significant variation for genotype, environment, and genotype by environment interaction effects and the highest contributor for the total variation was the environment (67.45 %) main effect followed by genotype by environment interaction (22.73 %) and the genotype (9.82 %). The first (44.11 %) and the second (26.79 %) interaction principal component axes were significant and cumulatively accounted for 70.91 % of the total interaction variance. Based on the first two additive main effects and multiplicative interaction analyses, E6, E4, and E2 were located far from the biplot origin and had a high contribution to the total variation of genotype by environment interaction. The fodder yield of the genotypes G6, G10, G23, G7, G9, G18, G13, G1, G24, G15, G2, G3, G19, G4, and G11 were above the grand mean. The fodder yield stability result obtained from the second additive main effects and multiplicative interaction is usually more accurate compared to the first additive main effects and multiplicative interaction. Consequently, genotypes which had mean fodder yield above the grand mean and relatively stable performance were observed for G23, G6, G10, G7, G11, G1, G9, G15, G2, G3, G4, and G19. However, G2, G3, G4, G11, and G19 have released varieties while the remaining genotypes have not been yet released. Therefore, G6, G10, G23, G7, and G9 were selected for verification and cultivation of these selected genotypes would contribute more for oat fodder yield improvement programs in Ethiopia.