Functional differences in forebrain auditory regions during learned vocal recognition in songbirds

Converging evidence implicates the auditory forebrain regions caudal medial mesopallium (formerly cmHV) and caudal medial nidopallium in the perceptual processing of conspecific vocalizations in songbirds. Little is known however, about more specific processing within these regions especially during song-based perceptual behaviors. One hallmark of the caudal medial mesopallium and caudal medial nidopallium, areas analogous to mammalian secondary auditory cortical structures, is their robust expression of the immediate-early-gene zenk in response to conspecific songs. Using European starlings operantly trained to recognize the songs of individual conspecifics, we show that the levels and patterns of zenk protein expression in the caudal medial nidopallium and caudal medial mesopallium differ when song recognition demands are placed on the system. In the caudal medial mesopallium, expression is significantly elevated above basal levels during the recognition of familiar songs, the acquisition of novel associations for familiar songs, and the acquisition of novel song discriminations. In the caudal medial nidopallium, however, expression is significantly elevated above basal levels only during the acquisition of novel song discriminations. The results directly implicate the caudal medial nidopallium and caudal medial mesopallium in at least a portion of the auditory processes underlying vocal recognition. Moreover, the observed differences between these regions imply the functional localization (or at least the concentration) of different auditory processing mechanisms within the caudal medial nidopallium and the caudal medial mesopallium.     

Bacterial classification with convolutional neural networks based on different data reduction layers

Abstract

For high accuracy classification of DNA sequences through Convolutional Neural Networks (CNNs), it is essential to use an efficient sequence representation that can accelerate similarity comparison between DNA sequences. In addition, CNN networks can be improved by avoiding the dimensionality problem associated with multi-layer CNN features. This paper presents a new approach for classification of bacterial DNA sequences based on a custom layer. A CNN is used with Frequency Chaos Game Representation (FCGR) of DNA. The FCGR is adopted as a sequence representation method with a suitable choice of the frequency k-lengthen words occurrence in DNA sequences. The DNA sequence is mapped using FCGR that produces an image of a gene sequence. This sequence displays both local and global patterns. A pre-trained CNN is built for image classification. First, the image is converted to feature maps through convolutional layers. This is sometimes followed by a down-sampling operation that reduces the spatial size of the feature map and removes redundant spatial information using the pooling layers. The Random Projection (RP) with an activation function, which carries data with a decent variety with some randomness, is suggested instead of the pooling layers. The feature reduction is achieved while keeping the high accuracy for classifying bacteria into taxonomic levels. The simulation results show that the proposed CNN based on RP has a trade-off between accuracy score and processing time.     

The efficient physiological strategy of a novel tomato genotype to adapt to chronic combined water and heat stress

• Climate change is increasing the frequency of high temperature shocks and water shortages, pointing to the need to develop novel tolerant varieties and to understand the mechanisms employed to withstand combined abiotic stresses.
• Two tomato genotypes, a heat-tolerant Solanum lycopersicum accession (LA3120) and a novel genotype (E42), previously selected as a stable yielding genotype under high temperatures, were exposed to single and combined water and heat stress. Plant functional traits, pollen viability and physiological (leaf gas exchange and chlorophyll a fluorescence emission measurements) and biochemical (antioxidant content and antioxidant enzyme activity) measurements were carried out. A Reduced Representation Sequencing approach allowed exploration of the genetic variability of both genotypes to identify candidate genes that could regulate stress responses.
• Both abiotic stresses had a severe impact on plant growth parameters and on the reproductive phase of development. Growth parameters and leaf gas exchange measurements revealed that the two genotypes used different physiological strategies to overcome individual and combined stresses, with E42 having a more efficient capacity to utilize the limiting water resources. Activation of antioxidant defence mechanisms seemed to be critical for both genotypes to counteract combined abiotic stresses. Candidate genes were identified that could explain the different physiological responses to stress observed in E42 compared with LA3120.
• Results here obtained have shown how new tomato genetic resources can be a valuable source of traits for adaptation to combined abiotic stresses and should be used in breeding programmes to improve stress tolerance in commercial varieties.