Neofunctionalization driven by positive selection led to the retention of the loqs2 gene encoding an Aedes specific dsRNA binding protein.

BACKGROUND: Mosquito borne viruses, such as dengue, Zika, yellow fever and Chikungunya, cause millions of infections every year. These viruses are mostly transmitted by two urban-adapted mosquito species, Aedes aegypti and Aedes albopictus. Although mechanistic understanding remains largely unknown, Aedes mosquitoes may have unique adaptations that lower the impact of viral infection. Recently, we reported the identification of an Aedes specific double-stranded RNA binding protein (dsRBP), named Loqs2, that is involved in the control of infection by dengue and Zika viruses in mosquitoes. Preliminary analyses suggested that the loqs2 gene is a paralog of loquacious (loqs) and r2d2, two co-factors of the RNA interference (RNAi) pathway, a major antiviral mechanism in insects. RESULTS: Here we analyzed the origin and evolution of loqs2. Our data suggest that loqs2 originated from two independent duplications of the first double-stranded RNA binding domain of loqs that occurred before the origin of the Aedes Stegomyia subgenus, around 31 million years ago. We show that the loqs2 gene is evolving under relaxed purifying selection at a faster pace than loqs, with evidence of neofunctionalization driven by positive selection. Accordingly, we observed that Loqs2 is localized mainly in the nucleus, different from R2D2 and both isoforms of Loqs that are cytoplasmic. In contrast to r2d2 and loqs, loqs2 expression is stage- and tissue-specific, restricted mostly to reproductive tissues in adult Ae. aegypti and Ae. albopictus. Transgenic mosquitoes engineered to express loqs2 ubiquitously undergo developmental arrest at larval stages that correlates with massive dysregulation of gene expression without major effects on microRNAs or other endogenous small RNAs, classically associated with RNA interference. CONCLUSIONS: Our results uncover the peculiar origin and neofunctionalization of loqs2 driven by positive selection. This study shows an example of unique adaptations in Aedes mosquitoes that could ultimately help explain their effectiveness as virus vectors.

Widespread selection and gene flow shape the genomic landscape during a radiation of monkeyflowers.

Speciation genomic studies aim to interpret patterns of genome-wide variation in light of the processes that give rise to new species. However, interpreting the genomic "landscape" of speciation is difficult, because many evolutionary processes can impact levels of variation. Facilitated by the first chromosome-level assembly for the group, we use whole-genome sequencing and simulations to shed light on the processes that have shaped the genomic landscape during a radiation of monkeyflowers. After inferring the phylogenetic relationships among the 9 taxa in this radiation, we show that highly similar diversity (π) and differentiation (FST) landscapes have emerged across the group. Variation in these landscapes was strongly predicted by the local density of functional elements and the recombination rate, suggesting that the landscapes have been shaped by widespread natural selection. Using the varying divergence times between pairs of taxa, we show that the correlations between FST and genome features arose almost immediately after a population split and have become stronger over time. Simulations of genomic landscape evolution suggest that background selection (BGS; i.e., selection against deleterious mutations) alone is too subtle to generate the observed patterns, but scenarios that involve positive selection and genetic incompatibilities are plausible alternative explanations. Finally, tests for introgression among these taxa reveal widespread evidence of heterogeneous selection against gene flow during this radiation. Combined with previous evidence for adaptation in this system, we conclude that the correlation in FST among these taxa informs us about the processes contributing to adaptation and speciation during a rapid radiation.

Hull and Aerial Holonomic Propulsion System Design for Optimal Underwater Sensor Positioning in Autonomous Surface Vessels.

Acoustic Doppler Current Profiler (ADCP) sensors measure water inflows and are essential to evaluate the Flow Curve (FC) of rivers. The FC is used to calibrate hydrological models responsible for planning the electrical dispatch of all power plants in several countries. Therefore, errors in those measures propagate to the final energy cost evaluation. One problem regarding this sensor is its positioning on the vessel. If placed on the bow, it becomes exposed to flowing obstacles, and if it is installed on the stern, the redirected water from the boat and its propulsion system change the sensor readings. To improve the sensor readings, this paper proposes the design of a catamaran-like Autonomous Surface Vessel (ASV) with an optimized hull design, aerial propulsion, and optimal sensor placement to keep them protected and precise, allowing inspections in critical areas such as ultra-shallow waters and mangroves.

Shared evolutionary processes shape landscapes of genomic variation in the great apes.

For at least the past 5 decades, population genetics, as a field, has worked to describe the precise balance of forces that shape patterns of variation in genomes. The problem is challenging because modeling the interactions between evolutionary processes is difficult, and different processes can impact genetic variation in similar ways. In this paper, we describe how diversity and divergence between closely related species change with time, using correlations between landscapes of genetic variation as a tool to understand the interplay between evolutionary processes. We find strong correlations between landscapes of diversity and divergence in a well-sampled set of great ape genomes, and explore how various processes such as incomplete lineage sorting, mutation rate variation, GC-biased gene conversion and selection contribute to these correlations. Through highly realistic, chromosome-scale, forward-in-time simulations, we show that the landscapes of diversity and divergence in the great apes are too well correlated to be explained via strictly neutral processes alone. Our best fitting simulation includes both deleterious and beneficial mutations in functional portions of the genome, in which 9% of fixations within those regions is driven by positive selection. This study provides a framework for modeling genetic variation in closely related species, an approach which can shed light on the complex balance of forces that have shaped genetic variation.

Shared evolutionary processes shape landscapes of genomic variation in the great apes.

For at least the past five decades population genetics, as a field, has worked to describe the precise balance of forces that shape patterns of variation in genomes. The problem is challenging because modelling the interactions between evolutionary processes is difficult, and different processes can impact genetic variation in similar ways. In this paper, we describe how diversity and divergence between closely related species change with time, using correlations between landscapes of genetic variation as a tool to understand the interplay between evolutionary processes. We find strong correlations between landscapes of diversity and divergence in a well sampled set of great ape genomes, and explore how various processes such as incomplete lineage sorting, mutation rate variation, GC-biased gene conversion and selection contribute to these correlations. Through highly realistic, chromosome-scale, forward-in-time simulations we show that the landscapes of diversity and divergence in the great apes are too well correlated to be explained via strictly neutral processes alone. Our best fitting simulation includes both deleterious and beneficial mutations in functional portions of the genome, in which 9% of fixations within those regions is driven by positive selection. This study provides a framework for modelling genetic variation in closely related species, an approach which can shed light on the complex balance of forces that have shaped genetic variation.

Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations.

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

Efficient ancestry and mutation simulation with msprime 1.0.

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime's many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

Genomic Responses to Climate Change: Making the Most of the Drosophila Model.

It is pressing to understand how animal populations evolve in response to climate change. We argue that new sequencing technologies and the use of historical samples are opening unprecedented opportunities to investigate genome-wide responses to changing environments. However, there are important challenges in interpreting the emerging findings. First, it is essential to differentiate genetic adaptation from phenotypic plasticity. Second, it is extremely difficult to map genotype, phenotype, and fitness. Third, neutral demographic processes and natural selection affect genetic variation in similar ways. We argue that Drosophila melanogaster, a classical model organism with decades of climate adaptation research, is uniquely suited to overcome most of these challenges. In the near future, long-term time series genome-wide datasets of D. melanogaster natural populations will provide exciting opportunities to study adaptation to recent climate change and will lay the groundwork for related research in non-model systems.

Preferential habitats prediction in syngnathids using species distribution models.

Syngnathids are considered as flagship species for marine conservation. Seahorses and pipefish are highly vulnerable to anthropogenic and environmental disturbances, but most species are currently considered Data Deficient by IUCN, requiring more biological and ecological research. Although syngnathids are well known for their unusual breeding biology, some aspects on the ecology of this family have rarely received attention. The knowledge on the factors governing syngnathids distribution is limited to some species and geographical regions. The present study is the first approach to predict syngnathid habitat preference in Spanish coasts, particularly in a marine National Park. In this study, Species Distribution Models (SDMs) were implemented to investigate the preferential habitat and distribution of the pipefish Syngnathus acus in Cíes Archipelago (Atlantic Islands of Galicia National Park, PNIA). Occurrence data of the species obtained from 2016 to 2018 surveys in PNIA were modeled as a function of bathymetric (depth, slope), substrate (sediment texture) and oceanographic (waves exposure) variables, using GAM, Random Forest and Maxent algorithms. From those SDMs, prediction models were built and the ensemble map of predictions was performed. The variables that most determined the distribution of the species were depth and wave exposure. The results of this study provide information on (1) habitat preference in the most dominant species in PNIA, the pipefish S. acus, towards sustainable management of this species in the National Park, and (2) predictive statistical tools for proper spatial conservation plans of this syngnathid species.

Clinal and seasonal changes are correlated in Drosophila melanogaster natural populations.

Spatial and seasonal variations in the environment are ubiquitous. Environmental heterogeneity can affect natural populations and lead to covariation between environment and allele frequencies. Drosophila melanogaster is known to harbor polymorphisms that change both with latitude and seasons. Identifying the role of selection in driving these changes is not trivial, because nonadaptive processes can cause similar patterns. Given the environment changes in similar ways across seasons and along the latitudinal gradient, one promising approach may be to look for parallelism between clinal and seasonal changes. Here, we test whether there is a genome-wide correlation between clinal and seasonal changes, and whether the pattern is consistent with selection. Allele frequency estimates were obtained from pooled samples from seven different locations along the east coast of the United States, and across seasons within Pennsylvania. We show that there is a genome-wide correlation between clinal and seasonal variations, which cannot be explained by linked selection alone. This pattern is stronger in genomic regions with higher functional content, consistent with natural selection. We derive a way to biologically interpret these correlations and show that around 3.7% of the common, autosomal variants could be under parallel seasonal and spatial selection. Our results highlight the contribution of natural selection in driving fluctuations in allele frequencies in natural fly populations and point to a shared genomic basis to climate adaptation that happens over space and time in D. melanogaster.

Author Correction: Removal of deep-sea sponges by bottom trawling in the Flemish Cap area: conservation, ecology and economic assessment.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Unmanned aerial vehicle for transmission line inspection using an extended Kalman filter with colored electromagnetic interference.

Strong electromagnetic fields such as those generated by power stations and transmission lines cause disturbances that affect the on-board sensors of an autonomous unmanned aerial vehicles (AUAVs) and may lead to aircraft instability. To mitigate this effect, we use an extended Kalman filter with colored noise. In addition to the traditional aircraft dynamics, this approach considers the electromagnetic fields of transmission lines and their position, electrical current, and tower topology. In this way, the filter can predict and correct the interference in the aircraft sensors, thereby guaranteeing flight stability even when the AUAV is very close to the electromagnetic sources. This approach enables the AUAV to operate closer to the transformers and transmission lines, thereby paving the way for better autonomous inspection performed by electrical companies and further development of new technologies. To prove the effectiveness of this approach, theoretical and practical results involving a survey of transmission lines are demonstrated.

Removal of deep-sea sponges by bottom trawling in the Flemish Cap area: conservation, ecology and economic assessment.

Deep-sea sponge grounds are vulnerable marine ecosystems, which through their benthic-pelagic coupling of nutrients, are of functional relevance to the deep-sea realm. The impact of fishing bycatch is here evaluated for the first time at a bathyal, sponge-dominated ecosystem in the high seas managed by the Northwest Atlantic Fisheries Organization. Sponge biomass surfaces created from research survey data using both random forest modeling and a gridded surface revealed 231,140 t of sponges in the area. About 65% of that biomass was protected by current fisheries closures. However, projections of trawling tracks estimated that the sponge biomass within them would be wiped out in just 1 year by the current level of fishing activity if directed on the sponges. Because these sponges filter 56,143 ± 15,047 million litres of seawater daily, consume 63.11 ± 11.83 t of organic carbon through respiration, and affect the turnover of several nitrogen nutrients, their removal would likely affect the delicate ecological equilibrium of the deep-sea benthic ecosystem. We estimated that, on Flemish Cap, the economic value associated with seawater filtration by the sponges is nearly double the market value of the fish catch. Hence, fishery closures are essential to reach sponge conservation goals as economic drivers cannot be relied upon.

Heterogeneous catalytic ozonation of benzothiazole aqueous solution promoted by volcanic sand.

This paper presents experimental results on the catalytic effect of volcanic sand on benzothiazole ozonation. Experiments were assessed at laboratory scale, in a differential circular flow reactor composed of a volcanic sand fixed bed column of 19 cm3 and a 1 dm3 storage tank, operated in batch mode at 20 degrees C and pH 2-7. Experimental results show that ozone self-decomposition is enhanced by the presence of volcanic sand at all pH. At pH>pH(PZC), the increase in aqueous ozone decay could be related to ozone interaction with strong Lewis acid on metal oxide surface sites of the volcanic sand. Ozone self-decomposition reactions occurring on the volcanic sand are less affected by the presence of radical scavengers. Benzothiazole removal by ozonation is also enhanced by the presence of volcanic sand. Moreover, the inhibitory effect of free radical scavengers is also impaired by volcanic sand, suggesting that strong Lewis acid surface sites play a key role on the reaction mechanism.

A multiple kernel classification approach based on a Quadratic Successive Geometric Segmentation methodology with a fault diagnosis case.

This work presents a new approach for solving classification and learning problems. The Successive Geometric Segmentation technique is applied to encapsulate large datasets by using a series of Oriented Bounding Hyper Box (OBHBs). Each OBHB is obtained through linear separation analysis and each one represents a specific region in a pattern's solution space. Also, each OBHB can be seen as a data abstraction layer and be considered as an individual Kernel. Thus, it is possible by applying a quadratic discriminant function, to assemble a set of nonlinear surfaces separating each desirable pattern. This approach allows working with large datasets using high speed linear analysis tools and yet providing a very accurate non-linear classifier as final result. The methodology was tested using the UCI Machine Learning repository and a Power Transformer Fault Diagnosis real scenario problem. The results were compared with different approaches provided by literature and, finally, the potential and further applications of the methodology were also discussed.

Quality control for microarray analysis of human brain samples: The impact of postmortem factors, RNA characteristics, and histopathology.

The quality of results from microarray studies depends on RNA quality, which can be significantly influenced by postmortem factors. The aim of this study was to determine which postmortem factors and/or RNA electropherogram characteristics best correspond to microarray output and can be used to prospectively screen RNA prior to microarray analysis. Total RNA was extracted (N=125) from gray and white matter of postmortem frontal and occipital lobe tissue, acquired from normal controls, and patients with schizophrenia, bipolar disorder or major depression. Electropherograms were generated by the Agilent BioAnalyzer 2100, allowing calculation of the 28S/18S ratio, the 18S/baseline peak ratio and the RNA Integrity Number (RIN). These values were compared to post-hybridization image analysis of Affymetrix microarrays. The postmortem variables correlated with some quality measures but could not be used as effective screening tools. Logistic regression demonstrated that all three electropherogram measures were predictive for microarray quality, and that the RIN threshold predictive of "good quality" (>35% present calls) was most consistent with that of prior studies. The optimal RIN must be determined by the investigator's specifications for false inclusion and false exclusion. In contrast to RIN, the quality threshold for the 28S/18S ratio has proven unacceptably variable, due to sensitivity to slight differences in protocol and/or tissue source. In conclusion, the measures we found useful as screening criteria do not replace the need to exclude samples after a microarray analysis is performed, as an acceptable percent call rate and other measures of microarray quality represent the desired endpoint.

Occurrence of Fusarium solani on Blueberry in Argentina.

Blueberry plants with root rot and sudden death symptoms were collected in Concordia, Entre Ríos. Diseased roots were disinfected by immersion in 0.5% NaOCl for 2 min, cut into pieces, transferred to carrot agar (CA), and maintained at 20 ± 2°C and 12 h of near UV light (Philips Black Light lamps TL 40W/08). Conidia were identified with an Olympus BX-51 optical microscope by using a CoolSNAP Pro digital kit with image-pro plus and color digital camera (Media Cybernetics, Inc., Silver Spring, MD). Macroconidia length was variable, 3.9 µm and as much as 29 µm (1 to 5 septa), and microconidia measured 3.8 × 11 µm. Fungal description agrees with Fusarium solani (1). Pathogenicity of the purified isolate was evaluated on 2-month-old plants (cvs. Misty and Sharp Blue). The purified, grayish white isolate was grown on CA for 7 days, and mycelial plugs were placed next to the base of wounded stem and roots immediately below the potting mix soil line. Plants were maintained in the dark at 20 ± 2°C and 90% humidity for 48 h, and then transferred to 12 h of light. Wounded plants with CA plugs served as controls. Dark spots along the stem and root and stem rot appeared 7 to 21 days after inoculation. Controls remained symptomless. The fungus was reisolated from inoculated plants. Fusarium sp. was previously cited (2). To our knowledge, this is the first report of F. solani on blueberry in Argentina. References: (1) C. Booth. Fusarium. Laboratory Guide to the Identification of the Major Species. CMI, Kew, England, 1977. (2) D. F. Farr et al. Fungi on Plants and Plant Products in the United States. The American Phytopathological Society, St. Paul, MN, 1989.

Exploring the QSAR's predictive truthfulness of the novel N-tuple discrete derivative indices on benchmark datasets.

Graph derivative indices (GDIs) have recently been defined over N-atoms (N = 2, 3 and 4) simultaneously, which are based on the concept of derivatives in discrete mathematics (finite difference), metaphorical to the derivative concept in classical mathematical analysis. These molecular descriptors (MDs) codify topo-chemical and topo-structural information based on the concept of the derivative of a molecular graph with respect to a given event (S) over duplex, triplex and quadruplex relations of atoms (vertices). These GDIs have been successfully applied in the description of physicochemical properties like reactivity, solubility and chemical shift, among others, and in several comparative quantitative structure activity/property relationship (QSAR/QSPR) studies. Although satisfactory results have been obtained in previous modelling studies with the aforementioned indices, it is necessary to develop new, more rigorous analysis to assess the true predictive performance of the novel structure codification. So, in the present paper, an assessment and statistical validation of the performance of these novel approaches in QSAR studies are executed, as well as a comparison with those of other QSAR procedures reported in the literature. To achieve the main aim of this research, QSARs were developed on eight chemical datasets widely used as benchmarks in the evaluation/validation of several QSAR methods and/or many different MDs (fundamentally 3D MDs). Three to seven variable QSAR models were built for each chemical dataset, according to the original dissection into training/test sets. The models were developed by using multiple linear regression (MLR) coupled with a genetic algorithm as the feature wrapper selection technique in the MobyDigs software. Each family of GDIs (for duplex, triplex and quadruplex) behaves similarly in all modelling, although there were some exceptions. However, when all families were used in combination, the results achieved were quantitatively higher than those reported by other authors in similar experiments. Comparisons with respect to external correlation coefficients (q2ext) revealed that the models based on GDIs possess superior predictive ability in seven of the eight datasets analysed, outperforming methodologies based on similar or more complex techniques and confirming the good predictive power of the obtained models. For the q2ext values, the non-parametric comparison revealed significantly different results to those reported so far, which demonstrated that the models based on DIVATI's indices presented the best global performance and yielded significantly better predictions than the 12 0-3D QSAR procedures used in the comparison. Therefore, GDIs are suitable for structure codification of the molecules and constitute a good alternative to build QSARs for the prediction of physicochemical, biological and environmental endpoints.

Ancient deep-sea sponge grounds on the Flemish Cap and Grand Bank, northwest Atlantic.

Recent studies on deep-sea sponges have focused on mapping contemporary distributions while little work has been done to map historical distributions; historical distributions can provide valuable information on the time frame over which species have co-evolved and may provide insight into the reasons for their persistence or decline. Members of the sponge family Geodiidae are dominant members of deep-sea sponge assemblages in the northwestern Atlantic. They possess unique spicules called sterrasters, which undergo little transport in sediment and can therefore indicate the Geodiidae sponge historical presence when found in sediment cores. This study focuses on the slopes of Flemish Cap and Grand Bank, important fishing grounds off the coast of Newfoundland, Canada, in international waters. Sediment cores collected in 2009 and 2010 were visually inspected for sponge spicules. Cores containing spicules were sub-sampled and examined under a light microscope for the presence of sterrasters. These cores were also dated using X-radiographs and grouped into five time categories based on known sediment horizons, ranging from 17,000 years BP to the present. Chronological groupings identified Geodiidae sponges in four persistent sponge grounds. The oldest sterrasters were concentrated in the eastern region of the Flemish Cap and on the southeastern slope of the Grand Bank. Opportunistic sampling of a long core in the southeastern region of the Flemish Cap showed the continuous presence of sponge spicules to more than 130 ka BP. Our results indicate that the geodiids underwent a significant range expansion following deglaciation, and support a contemporary distribution that is not shaped by recent fishing activity.

Evaluation of the efficacy of Grofactor, a beta-adrenergic agonist based on zilpaterol hydrochloride, using feedlot finishing bulls.

Beta-adrenergic agonists (ß-AA) have been shown to positively impact finishing performance and some carcass traits of feedlot cattle. Our objective was to evaluate the efficacy of a ß-AA on the basis of zilpaterol hydrochloride (Grofactor, Laboratorios Virbac México, Guadalajara, Mexico) on growth and DMI, carcass characteristics, and meat quality of finishing bulls. Forty-five bulls (75% 25% ) initially weighing 448.7 ± 2.58 kg were blocked by BW and randomly assigned to 1 of 3 diets, using pens of 3 animals, in a randomized complete block design: 1) daily feeding without ß-AA in the basal diet (Control), 2) daily feeding with 0.15 mg/kg BW of Grofactor added to the basal diet (ZHG), or 3) daily feeding with 0.15 mg/kg BW of Zilmax (MSD Salud Animal México, Mexico City, Mexico) added to the basal diet (ZHZ). The duration of the feeding period was 30 d with a subsequent 4-d withdrawal period. Compared with Control bulls, the group fed ZHG had a 12% better ( < 0.025) G:F ratio, and their final BW ( 0.094) and ADG ( 0.084) tended to be enhanced. Feedlot performance of ZHG and ZHZ bulls was similar, although the DMI was ∼4% lower ( 0.05) in ZHG bulls vs. the ZHZ and Control groups. The HCW ( 0.001) and dressing percentage ( 0.015) were higher by 20 kg and 3%, respectively, in ZHG bulls vs. Control bulls. The KPH fat was lower ( 0.007) in bulls fed ZHG than in nonsupplemented bulls, but other carcass characteristics were not different in the ZHG and ZHZ bulls, and noncarcass components were not affected by ZHG or ZHZ supplementation. At 48 h postmortem, ZHG bulls had lower ( 0.007) water holding capacity and trended toward ( 0.06) increased chroma and reduced pH ( 0.09) compared to Control bulls. However, compared to ZHZ bulls, ZHG bulls had higher ( 0.02) chroma and a trend ( 0.08) toward increased hue angle. At 14 d postmortem, meat quality variables did not differ between the 3 groups of bulls. Supplementation of ZH Grofactor improved feedlot performance and some carcass characteristics of finishing bulls without affecting meat quality. The effects of Grofactor on feedlot performance, carcass traits, and meat quality were similar to those of Zilmax.