Early environment influences later performance in fishes

Conditions fish encounter during embryogenesis and early life history can leave lasting effects not only on morphology, but also on growth rate, life‐history and behavioural traits. The ecology of offspring can be affected by conditions experienced by their parents and mother in particular. This review summarizes such early impacts and their ecological influences for a variety of teleost species, but with special reference to salmonids. Growth and adult body size, sex ratio, egg size, lifespan and tendency to migrate can all be affected by early influences. Mechanisms behind such phenotypically plastic impacts are not well known, but epigenetic change appears to be one central mechanism. The thermal regime during development and incubation is particularly important, but also early food consumption and intraspecific density can all be responsible for later life‐history variation. For behavioural traits, early experiences with effects on brain, sensory development and cognition appear essential. This may also influence boldness and other social behaviours such as mate choice. At the end of the review, several issues and questions for future studies are given.     

Analysis of conformational motions and related key residue interactions responsible for a specific function of proteins with elastic network model

Protein collective motions play a critical role in many biochemical processes. How to predict the functional motions and the related key residue interactions in proteins is important for our understanding in the mechanism of the biochemical processes. Normal mode analysis (NMA) of the elastic network model (ENM) is one of the effective approaches to investigate the structure-encoded motions in proteins. However, the motion modes revealed by the conventional NMA approach do not necessarily correspond to a specific function of protein. In the present work, a new analysis method was proposed to identify the motion modes responsible for a specific function of proteins and then predict the key residue interactions involved in the functional motions by using a perturbation approach. In our method, an internal coordinate that accounts for the specific function was introduced, and the Cartesian coordinate space was transformed into the internal/Cartesian space by using linear approximation, where the introduced internal coordinate serves as one of the axes of the coordinate space. NMA of ENM in this internal/Cartesian space was performed and the function-relevant motion modes were identified according to their contributions to the specific function of proteins. Then the key residue interactions important for the functional motions of the protein were predicted as the interactions whose perturbation largely influences the fluctuation along the internal coordinate. Using our proposed methods, the maltose transporter (MalFGK₂) from E. Coli was studied. The functional motions and the key residue interactions that are related to the channel-gating function of this protein were successfully identified.     

Type-3 functional response in limnetic suspension-feeders,as demonstrated by in situ grazing rates

Field-measured grazing rates (ml/animal/d) of cladocerans (mostly daphniids) and diaptomids were assembled from various published studies and plotted as a function of corresponding phytoplankton concentration (μg l⁻¹ f.w.). Filtering rates of both zooplankton groups initially increased with seston concentration until maximal grazing rates were observed at approximately 4 × 10² and 1 × 10² μg l⁻¹ for cladocerans and copepods, respectively; at higher algal concentrations, filtering rates of both declined as a function of food concentration. The shape of these curves are most consistent with Holling's (1966) Type 3 functional response. We found little support for the Type 3 functional response in published laboratory studies of Daphnia; most investigators report either a Type 1 or Type 2 response. The one study in which the Type 3 response was observed involved experiments where animals were acclimated at low food concentrations for 24 h, whereas those studies associated with response Types 1 or 2 had acclimation periods of only 1 to 3 h. We therefore assembled relevant data from the literature to examine the effect of acclimation period on the feeding rates of Daphnia at low food concentrations. In the absence of any acclimation, animals filtered at extremely low rates. After 2 h of acclimation, however, filtering rates increased 4 to 5-fold but declined again with longer durations; after > 70 h of pre-conditioning, filtering rates were almost as low as they had been with no acclimation. We also found little support for the Type 3 functional response in published studies of copepods. The only study associated with a Type 3 response involved a marine copepod that had been subjected to a starvation period of 48 h; however, an analysis of the effects of acclimation period did not yield conclusive evidence that filtering rates of freshwater copepods (Diaptomus and Eudiaptomus) decrease significantly with acclimation duration. The low filtering rates associated with long acclimation periods in laboratory experiments appears to be a direct result of animals becoming emaciated from prolonged exposure to low food concentrations, a situation which renders them incapable of high filtering rates. This may explain the Type 3 functional response for field cladocerans, since zooplankton in food-limiting situations are constantly exposed to low food concentrations, and would therefore have low body carbon and consequently less energy to filter-feed. We cannot, however, use this to explain the Type 3 response for field diaptomids, since copepods in the laboratory did not appear to lose body carbon even after 72 h of feeding at very low food levels, and there was inconclusive evidence that either Diaptomus or Eudiaptomus decrease their filtering rates with acclimation period. Although Incipient Limiting Concentrations (ILC) for Daphnia ranged from 1 to 8.5 × 10³ μg 1⁻¹, more than half of these fell between 1 and 3 × 10³ μg l⁻¹, bracketing the value of 2.7 × 10² μg l⁻¹ for field cladocerans. There was, however, a great deal of variation in reported maximum ingestion rates (MIR), maximum filtering rates (MFR) and ILC values for Daphnia magna. ILC values from the few laboratory studies of freshwater copepods ranged between 0.5 to 2.8 × 10³ μg 1⁻¹, and was higher than the ILC value of approximately 0.2 × 10³ μg l⁻¹ calculated for field populations of D. minutus. Generally, there was considerable agreement among laboratory studies regarding the shape of grazing-rate and ingestion-rate curves when data were converted to similar units and presented on standardized scales.     

Accuracy of predicting genomic breeding values for carcass merit traits in Angus and Charolais beef cattle

Accuracy of predicting genomic breeding values for carcass merit traits including hot carcass weight, longissimus muscle area (REA), carcass average backfat thickness (AFAT), lean meat yield (LMY) and carcass marbling score (CMAR) was evaluated based on 543 Angus and 400 Charolais steers genotyped on the Illumina BovineSNP50 Beadchip. For the genomic prediction within Angus, the average accuracy was 0.35 with a range from 0.32 (LMY) to 0.37 (CMAR) across different training/validation data‐splitting strategies and statistical methods. The within‐breed genomic prediction for Charolais yielded an average accuracy of 0.36 with a range from 0.24 (REA) to 0.46 (AFAT). The across‐breed prediction had the lowest accuracy, which was on average near zero. When the data from the two breeds were combined to predict the breeding values of either breed, the prediction accuracy averaged 0.35 for Angus with a range from 0.33 (REA) to 0.39 (CMAR) and averaged 0.33 for Charolais with a range from 0.18 (REA) to 0.46 (AFAT). The prediction accuracy was slightly higher on average when the data were split by animal's birth year than when the data were split by sire family. These results demonstrate that the genetic relationship or relatedness of selection candidates with the training population has a great impact on the accuracy of predicting genomic breeding values under the density of the marker panel used in this study.