In flies and humans, bitter chemicals are known to inhibit sugar detection, but the adaptive role of this inhibition is often overlooked. At best, this inhibition is described as contributing to the rejection of potentially toxic food, but no studies have addressed the relative importance of the direct pathway that involves activating bitter-sensitive cells versus the indirect pathway represented by the inhibition of sugar detection. Using toxins to selectively ablate or inactivate populations of bitter-sensitive cells, we assessed the behavioral responses of flies to sucrose mixed with strychnine (which activates bitter-sensitive cells and inhibits sugar detection) or with L-canavanine (which only activates bitter-sensitive cells). As expected, flies with ablated bitter-sensitive cells failed to detect L-canavanine mixed with sucrose in three different feeding assays (proboscis extension responses, capillary feeding, and two-choice assays). However, such flies were still able to avoid strychnine mixed with sucrose. By means of electrophysiological recordings, we established that bitter molecules differ in their potency to inhibit sucrose detection and that sugar-sensing inhibition affects taste cells on the proboscis and the legs. The optogenetic response of sugar-sensitive cells was not reduced by strychnine, thus suggesting that this inhibition is linked directly to sugar transduction. We postulate that sugar-sensing inhibition represents a mechanism in insects to prevent ingesting harmful substances occurring within mixtures.

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Validating statistical analysis methods for RNA sequencing (RNA-seq) experiments is a complex task. Researchers often find themselves having to decide between competing models or assessing the reliability of results obtained with a designated analysis program. Computer simulation has been the most frequently used procedure to verify the adequacy of a model. However, datasets generated by simulations depend on the parameterization and the assumptions of the selected model. Moreover, such datasets may constitute a partial representation of reality as the complexity or RNA-seq data is hard to mimic. We present the use of plasmode datasets to complement the evaluation of statistical models for RNA-seq data. A plasmode is a dataset obtained from experimental data but for which come truth is known. Using a set of simulated scenarios of technical and biological replicates, and public available datasets, we illustrate how to design algorithms to construct plasmodes under different experimental conditions. We contrast results from two types of methods for RNA-seq: (1) models based on negative binomial distribution (edgeR and DESeq), and (2) Gaussian models applied after transformation of data (MAANOVA). Results emphasize the fact that deciding what method to use may be experiment-specific due to the unknown distributions of expression levels. Plasmodes may contribute to choose which method to apply by using a similar pre-existing dataset. The promising results obtained from this approach, emphasize the need of promoting and improving systematic data sharing across the research community to facilitate plasmode building. Although we illustrate the use of plasmode for comparing differential expression analysis models, the flexibility of plasmode construction allows comparing upstream analysis, as normalization procedures or alignment pipelines, as well.

The upper region of the Río Negro and Neuquén valley, Argentina (latitude: 38 degrees 55 minutes South) experiences high temperatures and light intensities before the apple harvest. This hinders these fruits turning red and increases the risks of them becoming sunburnt. In the December of two growing seasons (when the fruits were about 43 mm in diameter), still some 80 days before harvest, 15% and 55% density shade nets were placed over "Fuji" apple trees. At harvest time, light distribution was determined at two canopy heights (1 and 3 m) on either side of the trees. Fruiting spurs were examined, and colour, sunburn damage, weight, soluble solid content and flesh firmness of the fruits determined. Specific leaf weight (SLW) was also established. Shade nets notably decreased the amount of photosynthetically active radiation (PAR) available; they also reduced fruit colour (redness), soluble solid content and flesh firmness, and the SLW. The 55% density net decreased fruit sunburn, but no differences were found between the 15% density net and control treatments. Spurs at the bottom of the canopy received less light, and the SLW, as well as the colour and soluble solid content of their fruit, was lower than observed for the higher spurs. The impossibility of exporting fruits damaged by high temperatures and intense solar radiation during ripening requires shade nets be used, their density depending on the conditions experienced.

By showing where and when a trunk-injected compound is distributed in the apple tree canopy, this study addresses a key knowledge gap in terms of explaining the efficiency of the compound in the crown. These findings allow the improvement of target-precise pesticide delivery for more sustainable tree-based agriculture.

Sample- and gene- based hierarchical cluster analyses have been widely adopted as tools for exploring gene expression data in high-throughput experiments. Gene expression values (read counts) generated by RNA sequencing technology (RNA-seq) are discrete variables with special statistical properties, such as over-dispersion and right-skewness. Additionally, read counts are subject to technology artifacts as differences in sequencing depth. This possesses a challenge to finding distance measures suitable for hierarchical clustering. Normalization and transformation procedures have been proposed to favor the use of Euclidean and correlation based distances. Additionally, novel model-based dissimilarities that account for RNA-seq data characteristics have also been proposed. Adequacy of dissimilarity measures has been assessed using parametric simulations or exemplar datasets that may limit the scope of the conclusions. Here, we propose the simulation of realistic conditions through creation of plasmode datasets, to assess the adequacy of dissimilarity measures for sample-based hierarchical clustering of RNA-seq data. Consistent results were obtained using plasmode datasets based on RNA-seq experiments conducted under widely different conditions. Dissimilarity measures based on Euclidean distance that only considered data normalization or data standardization were not reliable to represent the expected hierarchical structure. Conversely, using either a Poisson-based dissimilarity or a rank correlation based dissimilarity or an appropriate data transformation, resulted in dendrograms that resemble the expected hierarchical structure. Plasmode datasets can be generated for a wide range of scenarios upon which dissimilarity measures can be evaluated for sample-based hierarchical clustering analysis. We showed different ways of generating such plasmodes and applied them to the problem of selecting a suitable dissimilarity measure. We report several measures that are satisfactory and the choice of a particular measure may rely on the availability on the software pipeline of preference.

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