Plant-litter-soil feedbacks in common grass species are slightly negative and only marginally modified by litter exposed to insect herbivory

Purpose Insect herbivory affects plant growth, nutrient and secondary metabolite concentrations and litter quality. Changes to litter quality due to insect herbivory can alter decomposition, with knock on effects for plant growth mediated through the plant-litter-soil feedback pathway. Methods Using a multi-phase glasshouse experiment, we tested how changes in shoot and root litter quality of fast- and slow-growing grass caused by insect herbivores affect the performance of response plants in the soil in which the litter decomposed. Results We found that insect herbivory resulted in marginal changes to litter quality and did not affect growth when plants were grown with fast- versus slow-growing litter. Overall, presence of litter resulted in reduced root and shoot growth and this effect was significantly more negative in shoots versus roots. However, this effect was minimal, with a loss of c. 1.4% and 3.1% dry weight biomass in roots versus shoots, respectively. Further, shoot litter exposed to insect herbivory interacted with response plant identity to affect root growth. Conclusions Our results suggest that whether litter originates from plant tissues exposed to insect herbivory or not and its interaction with fast- versus slow-growing grasses is of little importance, but species-specific responses to herbivory-conditioned litter can occur. Taken collectively, the overall role of the plant-litter-soil feedback pathway, as well as its interaction with insect herbivory, is unlikely to affect broader ecosystem processes in this system

Innate differences in consumption and utilization of food by the fast- and slow-growing larvae of cabbage looper, Trichoplusia ni (Hübner) (Lep., Noctuidae)

Consumption and utilization of soybean leaves by the fast versus slow-growing larvae of the cabbage looper, Trichoplusia ni were studied under laboratory conditions. Fast-growing larvae consumed more food/unit of body mass, and gained weight 10 times faster than the slow-growing larvae. Slow-growing larvae were less efficient in consumption and utilization of food, and the differences in indices of food utilization were greater between the fast- and slow-growing larvae on the relatively resistant soybean cultivar, “PI 227687” than on the susceptible cultivar, “Davis”. Implications of differences in growth rates and food utilization by the fast- and slow-growing larvae on resistant and susceptible cultivars are discussed in relation to evolution of new biotypes and host-plant resistance

Machine learning-based prediction of breast cancer growth rate in-vivo

BackgroundDetermining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the rate of in vivo tumour growth using a unique study cohort of BC patients who had two serial mammograms wherein the tumour, visible in the diagnostic mammogram, was missed in the first screen.MethodsA serial mammography-derived in vivo growth rate (SM-INVIGOR) index was developed using tumour volumes from two serial mammograms and time interval between measurements. We then developed a machine learning-based surrogate model called Surr-INVIGOR using routinely assessed biomarkers to predict in vivo rate of tumour growth and extend the utility of this approach to a larger patient population. Surr-INVIGOR was validated using an independent cohort.ResultsSM-INVIGOR stratified discovery cohort patients into fast-growing versus slow-growing tumour subgroups, wherein patients with fast-growing tumours experienced poorer BC-specific survival. Our clinically relevant Surr-INVIGOR stratified tumours in the discovery cohort and was concordant with SM-INVIGOR. In the validation cohort, Surr-INVIGOR uncovered significant survival differences between patients with fast-growing and slow-growing tumours.ConclusionOur Surr-INVIGOR model predicts in vivo BC growth rate during the pre-diagnostic stage and offers several useful applications

RNA-Seq Identifies SNP Markers for Growth Traits in Rainbow Trout

Fast growth is an important and highly desired trait, which affects the profitability of food animal production, with feed costs accounting for the largest proportion of production costs. Traditional phenotype-based selection is typically used to select for growth traits; however, genetic improvement is slow over generations. Single nucleotide polymorphisms (SNPs) explain 90% of the genetic differences between individuals; therefore, they are most suitable for genetic evaluation and strategies that employ molecular genetics for selective breeding. SNPs found within or near a coding sequence are of particular interest because they are more likely to alter the biological function of a protein. We aimed to use SNPs to identify markers and genes associated with genetic variation in growth. RNA-Seq whole-transcriptome analysis of pooled cDNA samples from a population of rainbow trout selected for improved growth versus unselected genetic cohorts (10 fish from 1 full-sib family each) identified SNP markers associated with growth-rate. The allelic imbalances (the ratio between the allele frequencies of the fast growing sample and that of the slow growing sample) were considered at scores >5.0 as an amplification and <0.2 as loss of heterozygosity. A subset of SNPs (n = 54) were validated and evaluated for association with growth traits in 778 individuals of a three-generation parent/offspring panel representing 40 families. Twenty-two SNP markers and one mitochondrial haplotype were significantly associated with growth traits. Polymorphism of 48 of the markers was confirmed in other commercially important aquaculture stocks. Many markers were clustered into genes of metabolic energy production pathways and are suitable candidates for genetic selection. The study demonstrates that RNA-Seq at low sequence coverage of divergent populations is a fast and effective means of identifying SNPs, with allelic imbalances between phenotypes. This technique is suitable for marker development in non-model species lacking complete and well-annotated genome reference sequences

Growth–defence trade-off in rice: fast-growing and acquisitive genotypes have lower expression of genes involved in immunity

Plant ecologists and molecular biologists have long considered the hypothesis of a trade-off between plant growth and defence separately. In particular, how genes thought to control the growth–defence trade-off at the molecular level relate to trait-based frameworks in functional ecology, such as the slow–fast plant economics spectrum, is unknown. We grew 49 phenotypically diverse rice genotypes in pots under optimal conditions and measured growth-related functional traits and the constitutive expression of 11 genes involved in plant defence. We also quantified the concentration of silicon (Si) in leaves to estimate silica-based defences. Rice genotypes were aligned along a slow–fast continuum, with slow-growing, late-flowering genotypes versus fast-growing, early-flowering genotypes. Leaf dry matter content and leaf Si concentrations were not aligned with this axis and negatively correlated with each other. Live-fast genotypes exhibited greater expression of OsNPR1, a regulator of the salicylic acid pathway that promotes plant defence while suppressing plant growth. These genotypes also exhibited greater expression of SPL7 and GH3.2, which are also involved in both stress resistance and growth. Our results do not support the hypothesis of a growth–defence trade-off when leaf Si and leaf dry matter content are considered, but they do when hormonal pathway genes are considered. We demonstrate the benefits of combining ecological and molecular approaches to elucidate the growth–defence trade-off, opening new avenues for plant breeding and crop science

Fast and slow microphysics regimes in a minimalist model of cloudy Rayleigh-Bénard convection

A minimalist model of microphysical properties in cloudy Rayleigh-Bénard convection is developed based on mass and number balances for cloud droplets growing by vapor condensation. The model is relevant to a turbulent mixed-layer in which a steady forcing of supersaturation can be defined, e.g., a model of the cloudy boundary layer or a convection-cloud chamber. The model assumes steady injection of aerosol particles that are activated to form cloud droplets, and the removal of cloud droplets through sedimentation. Simplifying assumptions include the consideration of mean properties in steady state, neglect of coalescence growth, and no detailed representation of the droplet size distribution. Closed-form expressions for cloud droplet radius, number concentration, and liquid water content are derived. Limits of fast and slow microphysics, compared to the turbulent mixing time scale, are explored, and resulting expressions for the scaling of microphysical properties in fast and slow regimes are obtained. Scaling of microphysics with layer thickness is also explored, suggesting that liquid water content and cloud droplet number concentration increase, and mean droplet radius decreases with increasing layer thickness. Finally, the analytical model is shown to compare favorably to solutions of the fully-coupled set of governing ordinary differential equations that describe the system, and the predicted power law for liquid water mixing ratio versus droplet activation rate is observed to be consistent with measurements from the Pi convection-cloud chamber

Latino Settlement in the New Century

Identifies trends in the Hispanic/Latino population's growth and areas of settlement since 2000. Highlights faster growth in the West and Northeast and in metropolitan areas, slower growth in the Midwest, and the wider gender gap in faster-growing areas

Features of the Duckweed Lemna That Support Rapid Growth under Extremes of Light Intensity

This study addresses the unique functional features of duckweed via comparison of Lemna gibba grown under controlled conditions of 50 versus 1000 &micro;mol photons m&minus;2 s&minus;1 and of a L. minor population in a local pond with a nearby population of the biennial weed Malva neglecta. Principal component analysis of foliar pigment composition revealed that Malva was similar to fast-growing annuals, while Lemna was similar to slow-growing evergreens. Overall, Lemna exhibited traits reminiscent of those of its close relatives in the family Araceae, with a remarkable ability to acclimate to both deep shade and full sunlight. Specific features contributing to duckweed&rsquo;s shade tolerance included a foliar pigment composition indicative of large peripheral light-harvesting complexes. Conversely, features contributing to duckweed&rsquo;s tolerance of high light included the ability to convert a large fraction of the xanthophyll cycle pool to zeaxanthin and dissipate a large fraction of absorbed light non-photochemically. Overall, duckweed exhibited a combination of traits of fast-growing annuals and slow-growing evergreens with foliar pigment features that represented an exaggerated version of that of terrestrial perennials combined with an unusually high growth rate. Duckweed&rsquo;s ability to thrive under a wide range of light intensities can support success in a dynamic light environment with periodic cycles of rapid expansion. </div