Selection Experiments Research Articles

The effects of sex on extinction dynamics of Chlamydomonas reinhardtii depend on the rate of environmental change.

The continued existence of sex, despite many the costs it entails, still lacks an adequate explanation, as previous studies demonstrated that the effects of sex are environment-dependent: sex enhances the rate of adaptation in changing environments, but the benefits level off in benign conditions. To the best of our knowledge, the potential impact of different patterns of environmental change on the magnitude of these benefits received less attention in theoretical studies. In this paper, we begin to explore this issue by examining the effect of the rate of environmental deterioration (negatively correlated with population survival rate), on the benefits of sex. To investigate the interplay of sex and the rate of environmental deterioration, we carried out a long-term selection experiment with a unicellular alga (Chlamydomonas reinhardtii), by manipulating mode of reproduction (asexual, facultative or obligate sexual) and the rate of environmental deterioration (an increase of salt concentration). We monitored both the population size and extinction dynamics. The results revealed that the relative advantage of sex increased at the intermediate rate and plateaued at the highest rate of environmental deterioration. Obligate sexual populations had the slowest extinction rate under the intermediate rate of environmental deterioration, while facultative sexuality was favoured under the high rate-treatment. To the best of our knowledge, our study is the first to demonstrate that the interplay of sex and the rate of environmental deterioration affects the probability of survival, which indicates that mode of reproduction may be an important determinant of survival of the anthropogenic-induced environmental change.

A selective LSPR biosensor for molecular-level glycated albumin detection

A biosensor specifically engineered to detect glycated albumin (GA), a critical biomarker for diabetes monitoring, is presented. Unlike conventional GA monitoring methods, the biosensor herein uniquely employs localised surface plasmon resonance (LSPR) for signal transduction, leveraging a novel fabrication process where gold nanoparticles are deposited on a quartz substrate using flame spray pyrolysis. This enables the biosensor to provide mean glucose levels over a three-week period, correlating with the glycation status of diabetes patients. The sensor's DNA aptamer conjugation selectively binds GA, inducing a plasmonic wavelength shift; resulting in a detection limit of 0.1 μM, well within the human GA range of 20–240 μM. Selectivity experiments with diverse molecules and an exploration of sensor reusability were carried out with positive results. The novelty of the biosensor presented includes specificity, sensitivity and practical applicability; which is promising for enhanced diabetes diagnosis using a rapid and inexpensive process.

Research on the Integration Path of College Vocal Music Teaching and Traditional Music Culture Based on Deep Learning

Abstract In this paper, we first start from extracting music features and analyze the extraction methods for time-domain, frequency-domain, and cepstrum-domain features of music. Next, the logistic regression model is used to recognize music and deal with two-class and multi-class classification problems. To extract music and text features, a CNN-SVM-based classification model is proposed using a deep learning teaching practice structure. Finally, feature selection experiments are carried out on the extracted raw feature set using various feature selection algorithms to optimize the feature set and reduce classifier computation. The results show that the average number of feature items selected using the improved correlation coefficient method is 64, and the number of feature items is mostly distributed between 58 and 72. The data verifies that the improved correlation coefficient method proposed can effectively extract the features of traditional music, improve the accuracy of traditional music classification, and thus promote the development of the fusion of traditional music culture and vocal music teaching in colleges and universities.

Dual thermal ecotypes coexist within a nearly genetically identical population of the unicellular marine cyanobacterium Synechococcus.

The extent and ecological significance of intraspecific functional diversity within marine microbial populations is still poorly understood, and it remains unclear if such strain-level microdiversity will affect fitness and persistence in a rapidly changing ocean environment. In this study, we cultured 11 sympatric strains of the ubiquitous marine picocyanobacterium Synechococcus isolated from a Narragansett Bay (RI) phytoplankton community thermal selection experiment. Thermal performance curves revealed selection at cool and warm temperatures had subdivided the initial population into thermotypes with pronounced differences in maximum growth temperatures. Curiously, the genomes of all 11 isolates were almost identical (average nucleotide identities of >99.99%, with >99% of the genome aligning) and no differences in gene content or single nucleotide variants were associated with either cool or warm temperature phenotypes. Despite a very high level of genomic similarity, sequenced epigenomes for two strains showed differences in methylation on genes associated with photosynthesis. These corresponded to measured differences in photophysiology, suggesting a potential pathway for future mechanistic research into thermal microdiversity. Our study demonstrates that present-day marine microbial populations can harbor cryptic but environmentally relevant thermotypes which may increase their resilience to future rising temperatures.

A Filter-Based Improved Multi-Objective Equilibrium Optimizer for Single-Label and Multi-Label Feature Selection Problem

Effectively reducing the dimensionality of big data and retaining its key information has been a research challenge. As an important step in data pre-processing, feature selection plays a critical role in reducing data size and increasing the overall value of the data. Many previous studies have focused on single-label feature selection, however, with the increasing variety of data types, the need for feature selection on multi-label data types has also arisen. Unlike single-labeled data, multi-labeled data with more combinations of classifications place higher demands on the capabilities of feature selection algorithms. In this paper, we propose a filter-based Multi-Objective Equilibrium Optimizer algorithm (MOEO-Smp) to solve the feature selection problem for both single-label and multi-label data. MOEO-Smp rates the optimization results of solutions and features based on four pairs of optimization principles, and builds three equilibrium pools to guide exploration and exploitation based on the total scores of solutions and features and the ranking of objective fitness values, respectively. Seven UCI single-label datasets and two Mulan multi-label datasets and one COVID-19 multi-label dataset are used to test the feature selection capability of MOEO-Smp, and the feature selection results are compared with 10 other state-of-the-art algorithms and evaluated using three and seven different metrics, respectively. Feature selection experiments and comparisons with the results in other literatures show that MOEO-Smp not only has the highest classification accuracy and excellent dimensionality reduction on single-labeled data, but also performs better on multi-label data in terms of Hamming loss, accuracy, dimensionality reduction, and so on.

Single-Scan Ultraselective NMR Experiments with Preserved Sensitivity.

Selective NMR experiments provide rapid access to important structural information, and are essential to tackle the analysis of large molecules and complex mixtures. Single-scan ultraselective experiments are particularly useful, as they can rapidly select signals that overlap with other signals. Here, we describe a novel type of single-scan ultraselective NMR experiments that is robust against the effects of translational molecular diffusion, and thus make it possible to improve significantly the sensitivity of the experiment. This will largely broaden the applicability of this powerful class of experiments.

Single‐Scan Ultraselective NMR Experiments with Preserved Sensitivity

AbstractSelective NMR experiments provide rapid access to important structural information, and are essential to tackle the analysis of large molecules and complex mixtures. Single‐scan ultraselective experiments are particularly useful, as they can rapidly select signals that overlap with other signals. Here, we describe a novel type of single‐scan ultraselective NMR experiments that is robust against the effects of translational molecular diffusion, and thus make it possible to improve significantly the sensitivity of the experiment. This will largely broaden the applicability of this powerful class of experiments.

Three-Dimensional Porous PVDF Foam Imprinted Membranes with High Flux and Selectivity toward Artemisinin/Artemether.

In this study, a new 3D porous PVDF-foam-imprinted membrane (PPIM) for the selective separation of artemisinin (ART) was first prepared via the dopamine adhesion of pre-synthesized MIPs into the interior of the PPIM. In the PPIM, the pre-synthesized molecularly imprinted polymers (MIPs) with artesunate (ARU) as a dummy template were uniformly loaded on the interior of the membrane, avoiding the defects of recognition site encapsulation found in the conventional membrane. This membrane also exhibited excellent flux, which is beneficial in practical separation applications. The PPIM was systematically characterized via FT-IR, SEM, pore-size distribution analysis, water contact angle test, membrane flux, and mechanical performance analysis, respectively. In the static adsorption experiment, the pseudo-second-order kinetic model better fitted the rebinding data of ART. Under dynamic conditions, the ART adsorption capacity of the PPIM could be further remarkably improved by tailoring the flow rate to 3 mL min-1. In the selective separation experiment, with artemether (ARE) as the competition substrate, the selective separation ability (α) of the PPIM towards ART/artemether (ARE) reached its peak value (3.16) within only 10 min at this flow rate, which is higher than that of porous PVDF foam non-imprinted membranes (PPNM) (ca. 1.5), showing great separation efficiency in a short time. Moreover, the PPIM can be reused five times without a significant decrease in its adsorption capacities, showing good regeneration performance. This work highlights a simple strategy for constructing new MIMs with high flux and great mechanical strength to achieve the efficient selective separation of ART and ARE in practical applications.

Adaptive selection of members for convective-permitting regional ensemble prediction over the western Maritime Continent

A common issue faced by the downscaled regional ensemble prediction systems is the under-dispersiveness of the ensemble forecasts, often attributed to the lack of spread under the initial conditions from the global ensemble. In this study, a novel method that adopts an adaptive approach to selecting global ensemble members for regional downscaling has been developed. Instead of using a fixed set of pre-selected global ensemble members, the adaptive selection performs a sampling algorithm and selects the global ensemble members, which maximizes a fractions skill score (FSS)-based displacement between ensemble members. The method is applied to a convective-permitting ensemble prediction system over the western Maritime Continent, referred to as SINGV-EPS. SINGV-EPS has a grid spacing of 4.5 km and is a 12-member ensemble that is driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) 51-member global ensemble. Month-long trials were conducted in June 2020 to assess the impact of adaptive selection on the ensemble forecast spread and rainfall verification scores. In both fixed pre-selection and adaptive selection experiments, SINGV-EPS was still under-dispersive. However, adaptive selection improved the ensemble spread and reduced the root-mean-square error (RMSE) of the ensemble mean in wind, temperature, and precipitation fields. Further verification of the rainfall forecasts showed that there was a reduction in the Brier score and a higher hit rate in the relative operating characteristic (ROC) curve for all rainfall thresholds when adaptive selection was applied. Additionally, the ensemble mean forecasts from adaptive selection experiments are more accurate beyond 24 h, with a higher FSS for all rainfall thresholds and neighborhood lengths. These results suggest that the adaptive selection is superior to the fixed pre-selection of global ensemble members for downscaled regional ensemble prediction.

Effective removal of trace arsenic from groundwater by capacitive deionization

In this paper, functionalized CNTs and Fe3O4 were compounded by co-precipitation method and the binder-free capacitive deionization (CDI) electrodes were innovative prepared by electrophoretic deposition (EPD) technique. The effects of applied voltage, flow rate and solution pH on the adsorption capacity of arsenic were investigated. The results confirmed that introduction of different functional groups (–COOH, -SO3H and -PO3H2) and composite transition metal oxide (Fe3O4) led to the materials not only with enhanced hydrophilicity but also with higher capacitance. The CDI test showed that trace arsenic can be effective treated to less 2 μg/L, which satisfied the World Health Organization (WHO) drinking water standard (below 10 μg/L). The CNTs-X@Fe3O4 electrode was found to maintain specific adsorption of arsenic through selectivity experiments. In addition, the combined electro-sorption and chemisorption effectively produce higher adsorption capacity, faster adsorption rate and high selectivity. Therefore, the electrode prepared by EPD has a high prospect of application in arsenic removal by CDI.

Locomotor play behavior evolves by random genetic drift but not as a correlated response to selective breeding for high voluntary wheel-running behavior

Locomotor play is vigorous and seemingly purposeless behavior, commonly observed in young mammals. It can be costly in terms of energy expenditure, increased injury risk, and predator exposure. The main hypothesized benefit of locomotor play is enhancement of neuromuscular development, with effects persisting into adulthood. We hypothesized that levels of locomotor play would have evolved as a correlated response to artificial selection for increased voluntary exercise behavior. We studied mice from 4 replicate lines bred for voluntary wheel running (High Runner or HR) at 6–8 weeks of age and four non-selected Control (C) lines. Mice were weaned at 21 days of age and play behavior was observed for generations 20 (22–24 days old), 68 (22–23 days old), and 93 (15 days old). We quantified locomotor play as (1) rapid, horizontally directed jerk-run sequences and (2) vertical "bouncing." We used focal sampling to continuously record behavior in cages containing 4–6 individuals during the first 2–3 h of the dark cycle. Observations were significantly repeatable between observers and days. A two-way, mixed-model simultaneously tested effects of linetype (HR vs. C), sex, and their interaction. Contrary to our hypothesis, HR and C lines did not differ in any generation, nor did we find sex differences. However, differences among the replicate HR lines and among the replicate C lines were detected, and may be attributed to the effects of random genetic drift (and possibly founder effects). Thus, play behavior did evolve in this selection experiment, but not as a correlated response to selection for voluntary exercise.

Research on Feature Extraction of Underwater Acoustic Target Radiation Noise Based on Machine Learning Algorithm

Underwater acoustic target recognition is a very important technology in the field of underwater acoustics, with great economic and military value. Feature extraction technology for underwater acoustic target radiation noise signals is the key to achieving acoustic target recognition. This study aims at the feature extraction task of acoustic targets and extracts 10 types of 252-dimensional feature vectors from three domains: time domain, frequency domain, and auditory domain. Through 7 machine learning algorithms for classification and recognition experiments, the experimental results show that the recognition performance of the ensemble classifier is much better than that of a single classifier. For different types of features, this study combines three ensemble learning algorithms and feature selection algorithms to select the original 252-dimensional features. The feature selection experiment shows that the wrapper feature selection algorithm has the best effect, and the feature vector dimension can be reduced to 40 dimensions. The recognition accuracy rate is not less than 92.8%, which provides feature extraction guidance for acoustic target recognition based on feature extraction.

Hybrid experimental designs for joint estimation of absolute and relative retention curves for trawl gears

Abstract Methods for jointly estimating both the size-specific relative retention and absolute retention (retention probability) of two experimental trawl gears are considered. One approach would be to undertake comparative fishing of both gears, from which their comparative catchability can be estimated directly. If comparative catchability can be corrected for differing sampling effort and fishing efficiency of the two gears, then relative retention can be estimated. Furthermore, if a suitable functional form of the absolute retention curve is assumed, then estimates of absolute retention can potentially be obtained. However, these estimated retention curves are seen to have extremely large statistical variability and will not be useful in practice. On the other hand, if the gears are each fished with a non-selective control, then absolute retention can be directly estimated, from which estimates of relative retention can immediately be obtained. However, these estimates of relative retention have greater variability than would be obtained by comparative fishing with the same level of sampling effort. It is shown that a hybrid selectivity design that includes both comparative fishing of the experimental trawls and fishing with a non-selective control provides a more balanced approach. An extensive simulation study is conducted using scenarios based on recently published work on selectivity experiments targeting cod (Gadus Morhua) and hoki/blue grenadier (Macruronus novaezelandiae).

Supramolecular cellulose-based heavy metal adsorbent for efficient and accurate removal of cobalt (II) for water treatment

Heavy metal ions' high toxicity, carcinogenicity, and non-degradability have been identified as the most severe environmental concerns. Therefore, in this study, a new type of cellulose-based ionic imprinting adsorbent (p-CMs@EPI@PEI@Cys-IIP) with a three-dimensional pore structure was developed to selectively adsorb cobalt (II) using a simple ion-cross-linking technique. In this experiment, p-CMs@EPI@PEI@Cys-IIP were prepared by modifying cellulose microspheres (p-CMs) with epichlorohydrin (EPI), polyethyleneimine (PEI) and L-cysteine (L-Cys).The p-CMs@EPI@PEI@Cys-IIP was found to be able to selectively adsorb Co (II), and it was well characterized with FT-IR, SEM, TGA, and XRD. The system adsorption experiment proved that the maximum adsorption capacity reached 158.93 mg g-1, below the pH 7. After five cycles, the adsorption capacity of p-CMs@EPI@PEI@Cys-IIP still remained above 90%. The results of selectivity experiments indicated that p-CMs@EPI@PEI@Cys-IIP has preferential selectivity for Co (II). The adsorption isotherm and kinetic experiment of Co (II) conformed to the Langmuir model and pseudo-second-order kinetic, respectively. The adsorption process of Co (II) on p-CMs@EPI@PEI@Cys-IIP was also affected by the intra-particle diffusion model. Due to its simple preparation method and good adsorption performance, p-CMs@EPI@PEI@Cys-IIP would have a good application prospect in the selective removal of Co (II) for water treatment.

Realized genomic selection across generations in a reciprocal recurrent selection breeding program of Eucalyptus hybrids.

Genomic selection (GS) experiments in forest trees have largely reported estimates of predictive abilities from cross-validation among individuals in the same breeding generation. In such conditions, no effects of recombination, selection, drift, and environmental changes are accounted for. Here, we assessed the effectively realized predictive ability (RPA) for volume growth at harvest age by GS across generations in an operational reciprocal recurrent selection (RRS) program of hybrid Eucalyptus. Genomic best linear unbiased prediction with additive (GBLUP_G), additive plus dominance (GBLUP_G+D), and additive single-step (HBLUP) models were trained with different combinations of growth data of hybrids and pure species individuals (N = 17,462) of the G1 generation, 1,944 of which were genotyped with ~16,000 SNPs from SNP arrays. The hybrid G2 progeny trial (HPT267) was the GS target, with 1,400 selection candidates, 197 of which were genotyped still at the seedling stage, and genomically predicted for their breeding and genotypic values at the operational harvest age (6 years). Seedlings were then grown to harvest and measured, and their pedigree-based breeding and genotypic values were compared to their originally predicted genomic counterparts. Genomic RPAs ≥0.80 were obtained as the genetic relatedness between G1 and G2 increased, especially when the direct parents of selection candidates were used in training. GBLUP_G+D reached RPAs ≥0.70 only when hybrid or pure species data of G1 were included in training. HBLUP was only marginally better than GBLUP. Correlations ≥0.80 were obtained between pedigree and genomic individual ranks. Rank coincidence of the top 2.5% selections was the highest for GBLUP_G (45% to 60%) compared to GBLUP_G+D. To advance the pure species RRS populations, GS models were best when trained on pure species than hybrid data, and HBLUP yielded ~20% higher predictive abilities than GBLUP, but was not better than ABLUP for ungenotyped trees. We demonstrate that genomic data effectively enable accurate ranking of eucalypt hybrid seedlings for their yet-to-be observed volume growth at harvest age. Our results support a two-stage GS approach involving family selection by average genomic breeding value, followed by within-top-families individual GS, significantly increasing selection intensity, optimizing genotyping costs, and accelerating RRS breeding.

Selective solvent extraction of Cu(II) from aqueous solutions using an acyl-based thiourea: Extraction study and DFT analysis of reaction mechanism

Currently, copper-containing wastewater poses a great threat to the surrounding living areas due to its toxic and non-biodegradable properties. The solvent extraction technique for removing copper(II) from wastewater has been identified as a straightforward and effective method. In this study, novel acyl-containing thiourea extractants, namely N-benzoyl-N′,N′-diethyl thiourea (BDETU), N-benzoyl-N′,N′-dibutyl thiourea (BDBTU) and N-benzoyl-N′-butyl thiourea (BBTU), were prepared and employed as extractants for the efficient removal of Cu(II) ions from aqueous solution. Experimental results indicated that the highest extraction efficiency of Cu(II) by BDETU was 99.4% under the optimal extraction conditions, which surpassed the performance of both BDBTU and BBTU. The reusability and selectivity experiments further confirmed the enduring high extraction efficiency of BDETU over 8 consecutive extraction-stripping cycles. These findings suggest that BDETU exhibits promising prospects for wide-ranging applications owing to its excellent extraction stability and selectivity. The investigation into the extraction mechanism of Cu(II) with acyl thiourea molecule revealed that one mole of Cu(II) was extracted with two moles of extractants through the formation of Cu-O-C and Cu-S-C bonds.

Surface modification of lithium-ion sieves by silane coupling agent for improved adsorption performance

Solid-phase reaction is a promising method to synthesize lithium-ion sieves H2TiO3 (HTO) for simple production process. However, the uneven mixing of raw materials during the solid-phase reaction causes the agglomeration phenomenon. In this work, silane coupling agent (3-aminopropyl)triethoxysilane (KH550) was employed for surface modification of HTO to yield the HTO/KH550 composite by forming covalent bond. The crystallinity, composition, morphology, and porosity of HTO/KH550 were characterized by a series of techniques. The agglomeration phenomenon was well alleviated and HTO/KH550 showed increased surface area. The adsorption experiments were detailed carried out by varying the different factors. HTO/KH550 exhibited improved lithium adsorption capacity and adsorption rate constant (25.61 mg·g−1 and 0.0037 mg·g−1·h−1 vs 22.41 mg·g−1 and 0.0020 mg·g−1·h−1 of HTO) derived from surface modification by KH550. Ion exchange mechanism in the adsorption process was revealed by X-ray photoelectron spectroscopy (XPS). Selective adsorption experiments were performed and HTO/KH550 exhibited the partition coefficient (Kd) of Li+ much higher than the competing ions (Na+, Mg2+, K+, and Ca2+). Furthermore, HTO/KH550 showed excellent cyclic stability with the adsorption capacity loss of only 3.3 % after five adsorption–desorption cycles. This work is of guiding significance to synthesize surface modified lithium-ion sieves for industrial production.

In Vitro Synergistic Inhibitory Effects of Plant Extract Combinations on Bacterial Growth of Methicillin-Resistant Staphylococcus aureus.

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most common pathogens of healthcare-associated infections. Medicinal plants have long been used in the traditional treatment of diseases or syndromes worldwide. Combined use of plant extracts could improve the effectiveness of pharmacological action by obtaining synergism, acting on multiple targets simultaneously, reducing the doses of individual components, and minimizing side effects. We aimed to investigate the synergistic inhibitory effects of selected medicinal plants (Caesalpinia sappan L. (CS), Glycyrrhiza uralensis Fisch. (GU), Sanguisorba officinalis L. (SO), and Uncaria gambir Roxb. (UG)) on the bacterial growth of MRSA and its clinical isolates. SO and UG extracts generated the best synergistic interaction as adjudged by checkerboard synergy assays. MICs of the individual extracts decreased 4-fold from 250 to 62.5 μg/mL, respectively. The SO + UG combination was further evaluated for its effects on bacterial growth inhibition, minimum bactericidal/inhibitory concentration (MBC/MIC) ratio, and time-kill kinetics. The results indicate that the SO + UG combination synergistically inhibited the bacterial growth of MRSA strains with bactericidal effects. SO + UG combination also exhibited more potent effects against clinical isolates. In multistep resistance selection experiments, both standard and isolates of MRSA showed no resistance to the SO + UG combination even after repeated exposure over fourteen passages. Our data suggest that using plant extract combinations could be a potential strategy to treat MRSA infections.

New insights on the preparation of amine covalent organic polymer and its adsorption properties

Dye pollution is becoming increasingly severe. This study used the Schiff base reaction to synthesize a polyaromatic ring covalent organic polymer material with amide bonds and high electronegativity named SLEL-9 to adsorb Methylene Blue (MB) and Rhodamine B (RhB). SLEL-9 was characterized by Fourier transform infra-red spectra, X-ray photoelectron spectra, Brunauer-Emmett-Teller (BET), zeta potential analysis, and other techniques. It was found that SLEL-9 material contains C–C, CN, C–N, and CO. SLEL-9 had a zeta potential of about −45 mV under neutral conditions, which proved that the material had been synthesized successfully. The BET and Langmuir surface areas of SLEL-9 were 35.187 m2 g−1 and 56.419 m2 g−1, respectively. The adsorptions of SLEL-9 on low concentration (10 mg L−1) Methylene Blue and Rhodamine B reached equilibrium within 48 h. The results showed that SLEL-9's adsorption of dye molecules are more consistent with pseudo-second-order kinetic and Langmuir isotherm model. The adsorption experiments showed that the adsorption process is a spontaneous endothermic reaction, mainly chemisorption. The maximum adsorption capacity of SLEL-9 for MB and RhB were 132.45 mg g−1 and 101.94 mg g−1. In addition, this study investigated to determine the optimal reaction parameters. The primary mechanisms of SLEL-9 adsorption of two dyes are n→π* interaction, π-π EDA interaction and electrostatic attraction. Selective adsorb ability experiment results showed that SLEL-9 could selectively adsorb MB and RhB to a certain extent. Finally, it was found that SLEL-9 can maintain over 70% adsorption capacity after five reuses and can maintain stability after soaking in different pH water and organic solvents for 120 h. SLEL-9 proved to be a promising organic covalent polymer adsorption material for the removal of Methylene Blue and Rhodamine B in water.

Pervasive G × E interactions shape adaptive trajectories and the exploration of the phenotypic space in artificial selection experiments.

Quantitative genetics models have shown that long-term selection responses depend on initial variance and mutational influx. Understanding limits of selection requires quantifying the role of mutational variance. However, correlative responses to selection on nonfocal traits can perturb the selection response on the focal trait; and generations are often confounded with selection environments so that genotype by environment (G×E) interactions are ignored. The Saclay divergent selection experiments (DSEs) on maize flowering time were used to track the fate of individual mutations combining genotyping data and phenotyping data from yearly measurements (DSEYM) and common garden experiments (DSECG) with four objectives: (1) to quantify the relative contribution of standing and mutational variance to the selection response, (2) to estimate genotypic mutation effects, (3) to study the impact of G×E interactions in the selection response, and (4) to analyze how trait correlations modulate the exploration of the phenotypic space. We validated experimentally the expected enrichment of fixed beneficial mutations with an average effect of +0.278 and +0.299 days to flowering, depending on the genetic background. Fixation of unfavorable mutations reached up to 25% of incoming mutations, a genetic load possibly due to antagonistic pleiotropy, whereby mutations fixed in the selection environment (DSEYM) turned to be unfavorable in the evaluation environment (DSECG). Global patterns of trait correlations were conserved across genetic backgrounds but exhibited temporal patterns. Traits weakly or uncorrelated with flowering time triggered stochastic exploration of the phenotypic space, owing to microenvironment-specific fixation of standing variants and pleiotropic mutational input.