Filtering of visual information in the tectum by an identified neural circuit

The optic tectum of zebrafish is involved in behavioral responses that require the detection of small objects. The superficial layers of the tectal neuropil receive input from retinal axons, while its deeper layers convey the processed information to premotor areas. Imaging with a genetically encoded calcium indicator revealed that the deep layers, as well as the dendrites of single tectal neurons, are preferentially activated by small visual stimuli. This spatial filtering relies on GABAergic interneurons (using the neurotransmitter γ-aminobutyric acid) that are located in the superficial input layer and respond only to large visual stimuli. Photo-ablation of these cells with KillerRed, or silencing of their synaptic transmission, eliminates the size tuning of deeper layers and impairs the capture of prey.     

Resistance of postharvest biocontrol yeasts to oxidative stress: a possible new mechanism of action

ABSTRACT We detected the generation of the reactive oxygen species (ROS) superoxide anion ( O.(-) (2)) and hydrogen peroxide (H(2)O(2)) in apple wounds 2 immediately after wounding, and assessed the relationships between (i) timely colonization of apple wounds by biocontrol yeasts, (ii) resistance of these microorganisms to oxidative stress caused by ROS, and (iii) their antagonism against postharvest wound pathogens. We analyzed a model system consisting of two yeasts with higher (Cryptococcus laurentii LS-28) or lower (Rhodotorula glutinis LS-11) antagonistic activity against the postharvest pathogens Botrytis cinerea and Penicillium expansum. LS-28 exhibited faster and greater colonization of wounds than LS-11. In contrast to LS-28, the number of LS-11 cells dropped 1 and 2 h after application, and then increased only later. In vitro, LS-28 was more resistant to ROS-generated oxidative stress. The combined application of biocontrol yeasts and ROS-deactivating enzymes in apple wounds prevented the decrease in number of LS-11 cells mentioned above, and enhanced colonization and antagonistic activity of both biocontrol yeasts against B. cinerea and P. expansum. Polar lipids of LS-11 contained the more unsaturated and oxidizable alpha-linolenic acid, which was absent in LS-28. Resistance to oxidative stress could be a key mechanism of biocontrol yeasts antagonism against postharvest wound pathogens.     

Photolysis of pesticides: influence of epicuticular waxes from Persica laevis DC on the photodegradation in the solid phase of aminocarb,methiocarb and fenthion

Pesticides with N,N‐dimethyl and thiomethyl moieties (aminocarb, methiocarb and fenthion) were irradiated under artificial light (λ > 290 nm) in an amorphous wax phase from Persica laevis DC. The effect of the presence of the wax on the photolysis rate differed in the three pesticides, increasing it in aminocarb, having little effect in methiocarb and slowing it down in fenthion. The presence of the wax affected the qualitative photodegradation behaviour of all the pesticides. The data obtained were compared with those for pirimicarb, which had been studied earlier. © 2001 Society of Chemical Industry     

“Noisy beets”: impact of phenotyping errors on genomic predictions for binary traits in <Emphasis Type="Italic">Beta vulgaris</Emphasis>

Background

Noise (errors) in scientific data is endemic and may have a detrimental effect on statistical analyses and experimental results. The effects of noisy data have been assessed in genome-wide association studies for case-control experiments in human medicine. Little is known, however, on the impact of noisy data on genomic predictions, a widely used statistical application in plant and animal breeding.

Results

In this study, the sensitivity to noise in the data of five classification methods (K-nearest neighbours—KNN, random forest—RF, ridge logistic regression—LR, and support vector machines with linear or radial basis function kernels) was investigated. A sugar beet population of 123 plants phenotyped for a binary trait and genotyped for 192 SNP (single nucleotide polymorphism) markers was used. Labels (0/1 phenotype) were randomly sampled to generate noise. From the base scenario without errors in the labels, increasing proportions of noisy labels—up to 50 %—were generated and introduced in the data.

Conclusions

Local classification methods—KNN and RF—showed higher tolerance to noisy labels compared to methods that leverage global data properties—LR and the two SVM models. In particular, KNN outperformed all other classifiers with AUC (area under the ROC curve) higher than 0.95 up to 20 % noisy labels. The runner-up method, RF, had an AUC of 0.941 with 20 % noise.

     

Genome scan for the possibility of identifying candidate resistance genes for goat lentiviral infections in the Italian Garfagnina goat breed

Tropical Animal Health and Production - Small ruminant lentiviruses (SRLVs) are a heterogeneous group of viruses of sheep, goat, and wild ruminants responsible of lifelong persistent infection...     

Chemical composition and antimicrobial activity of the essential oil from Ferula glauca L. (F. communis L. subsp. glauca) growing in Marche (central Italy)

The essential oil obtained from different parts of Ferula glauca L. (formerly considered as a subspecies of F. communis) growing in Marche (central Italy), was analyzed for the first time by GC-FID and GC-MS. The major volatiles were (E)-caryophyllene and caryophyllene oxide in leaves, alpha-pinene, myrcene and germacrene D in flowers, alpha- and beta-pinene in fruits, (E)-beta-farnesene, myristicin and elemicin in roots, respectively. The differences in composition detected with respect to F. communis, made the volatile fraction a reliable marker to distinguish between them, and confirm the botanical data at the base of their discrimination. Furthermore, the oil was assayed for its antimicrobial activity by the broth microdilution method. B. subtilis was found to be the most sensitive microorganism, with the lowest MIC values.     

The potential for mitigation of methane emissions in ruminants through the application of metagenomics,metabolomics, and other -OMICS technologies

Ruminant supply chains contribute 5.7 gigatons of CO_2-eq per annum, which represents approximately 80% of the livestock sector emissions. One of the largest sources of emission in the ruminant sector is methane (CH₄), accounting for approximately 40% of the sectors total emissions. With climate change being a growing concern, emphasis is being put on reducing greenhouse gas emissions, including those from ruminant production. Various genetic and environmental factors influence cattle CH₄ production, such as breed, genetic makeup, diet, management practices, and physiological status of the host. The influence of genetic variability on CH₄ yield in ruminants indicates that genomic selection for reduced CH₄ emissions is possible. Although the microbiology of CH₄ production has been studied, further research is needed to identify key differences in the host and microbiome genomes and how they interact with one another. The advancement of “-omics” technologies, such as metabolomics and metagenomics, may provide valuable information in this regard. Improved understanding of genetic mechanisms associated with CH₄ production and the interaction between the microbiome profile and host genetics will increase the rate of genetic progress for reduced CH₄ emissions. Through a systems biology approach, various “-omics” technologies can be combined to unravel genomic regions and genetic markers associated with CH₄ production, which can then be used in selective breeding programs. This comprehensive review discusses current challenges in applying genomic selection for reduced CH₄ emissions, and the potential for “-omics” technologies, especially metabolomics and metagenomics, to minimize such challenges. The integration and evaluation of different levels of biological information using a systems biology approach is also discussed, which can assist in understanding the underlying genetic mechanisms and biology of CH₄ production traits in ruminants and aid in reducing agriculture’s overall environmental footprint.