Developmental Deformity Due to scalloped Non‐Function in Drosophila Brain Leads to Cognitive Impairment

Neural identity and wiring specificity are fundamental to brain function. Factors affecting proliferation of the progenitor cells leading to an expansion or regression of specific neuronal clusters are expected to challenge the process of formation of precise synaptic connections with their partners and their further integration to result in proper functional neural circuitry. We have investigated the role of scalloped, a Hippo pathway gene in Drosophila brain development and have shown that its function is critical to regulate proliferation of Mushroom Body Neuroblasts and to limit the neuronal cluster size to normal in the fly brain. Here we investigate the consequent effect of the anatomical phenotype of mutant flies on the brain function, as exemplified by their cognitive performance. We demonstrate that the neural expansion in important neural clusters of the olfactory pathway, caused due to Scalloped inactivation, imparts severe disabilities in learning, short‐term memory and long‐term memory. Scalloped knockdown in αβ Kenyon Cell clusters drastically reduces long‐term memory performance. Scalloped deficiency induced neural expansion in antennal lobe and ellipsoid body neurons bring down short‐term memory performance significantly. We also demonstrate that the cognitive impairments observed here are not due to a problem in memory formation or execution in the adult, but are due to the developmental deformities caused in the respective class of neurons. Our results strongly indicate that the additional neurons generated by Scalloped inactivation are not synergistically integrated into, but rather perturb the formation of precise functional circuitry.     

Female Social Feedback Reveals Non-imitative Mechanisms of Vocal Learning in Zebra Finches

1. Download high-res image (269KB)
2. Download full-size image

     

Integrating life history traits into predictive phylogeography

Predictive phylogeography seeks to aggregate genetic, environmental and taxonomic data from multiple species in order to make predictions about unsampled taxa using machine‐learning techniques such as Random Forests. To date, organismal trait data have infrequently been incorporated into predictive frameworks due to difficulties inherent to the scoring of trait data across a taxonomically broad set of taxa. We refine predictive frameworks from two North American systems, the inland temperate rainforests of the Pacific Northwest and the Southwestern Arid Lands (SWAL), by incorporating a number of organismal trait variables. Our results indicate that incorporating life history traits as predictor variables improves the performance of the supervised machine‐learning approach to predictive phylogeography, especially for the SWAL system, in which predictions made from only taxonomic and climate variables meets only moderate success. In particular, traits related to reproduction (e.g., reproductive mode; clutch size) and trophic level appear to be particularly informative to the predictive framework. Predictive frameworks offer an important mechanism for integration of organismal trait, environmental data, and genetic data in phylogeographic studies.     

应用多种教学方法,建设活跃的生物化学课程

生物化学授课过程中,教师通过丰富课堂资源、进行问题导向式教学、加强在线学习、建立多元化的课程考核方式及设定定时答疑制度等多种方法,激发学生学习兴趣,提升生物化学教学和学习效果,建设"笑起来""动起来"的活跃的课程。同学们普遍认为,生物化学课程内容充实有趣,教学形式多样,并且在课堂上学到了很多前沿知识。     

Social effects on fruit fly courtship song

Courtship behavior in Drosophila has often been described as a classic innate behavioral repertoire, but more recently extensive plasticity has been described. In particular, prior exposure to acoustic signals of con‐ or heterspecific males can change courtship traits in both sexes that are liable to be important in reproductive isolation. However, it is unknown whether male courtship song itself is socially plastic. We examined courtship song plasticity of two species in the Drosophila melanogaster subgroup. Sexual isolation between the species is influenced by two male song traits, the interpulse interval (IPI) and sinesong frequency (SSF). Neither of these showed plasticity when males had prior experience of con‐ and heterospecific social partners. However, males of both species produced longer bursts of song during courtship when they were exposed to social partners (either con‐ or heterospecific) than when they were reared in isolation. D. melanogaster carrying mutations affecting short‐ or medium‐term memory showed a similar response to the social environment, not supporting a role for learning. Our results demonstrate that the amount of song a male produces during courtship is plastic depending on the social environment, which might reflect the advantage of being able to respond to variation in intrasexual competition, but that song structure itself is relatively inflexible, perhaps due to strong selection against hybridization.     

Breeding habitat and nest‐site selection by an obligatory “nest‐cleptoparasite”, the Amur Falcon Falco amurensis

The selection of a nest site is crucial for successful reproduction of birds. Animals which re‐use or occupy nest sites constructed by other species often have limited choice. Little is known about the criteria of nest‐stealing species to choose suitable nesting sites and habitats. Here, we analyze breeding‐site selection of an obligatory “nest‐cleptoparasite”, the Amur Falcon Falco amurensis. We collected data on nest sites at Muraviovka Park in the Russian Far East, where the species breeds exclusively in nests of the Eurasian Magpie Pica pica. We sampled 117 Eurasian Magpie nests, 38 of which were occupied by Amur Falcons. Nest‐specific variables were assessed, and a recently developed habitat classification map was used to derive landscape metrics. We found that Amur Falcons chose a wide range of nesting sites, but significantly preferred nests with a domed roof. Breeding pairs of Eurasian Hobby Falco subbuteo and Eurasian Magpie were often found to breed near the nest in about the same distance as neighboring Amur Falcon pairs. Additionally, the occurrence of the species was positively associated with bare soil cover, forest cover, and shrub patches within their home range and negatively with the distance to wetlands. Areas of wetlands and fallow land might be used for foraging since Amur Falcons mostly depend on an insect diet. Additionally, we found that rarely burned habitats were preferred. Overall, the effect of landscape variables on the choice of actual nest sites appeared to be rather small. We used different classification methods to predict the probability of occurrence, of which the Random forest method showed the highest accuracy. The areas determined as suitable habitat showed a high concordance with the actual nest locations. We conclude that Amur Falcons prefer to occupy newly built (domed) nests to ensure high nest quality, as well as nests surrounded by available feeding habitats.     

Electrical Compartmentalization in Neurons

1. Download high-res image (192KB)
2. Download full-size image

     

MarkerML – Marker Feature Identification in Metagenomic Datasets Using Interpretable Machine Learning

Identification of environment specific marker-features is one of the key objectives of many metagenomic studies. It aims to identify such features in microbiome datasets that may serve as markers of the contrasting or comparable states. Hypothesis testing and black-box machine learnt models which are conventionally used for identification of these features are generally not exhaustive, especially because they generally do-not provide any quantifiable relevance (context) of/between the identified features. We present MarkerML web-server, that seeks to leverage the emergence of interpretable machine learning for facilitating the contextual discovery of metagenomic features of interest. It does so through a comprehensive and automated application of the concept of Shapley Additive Explanations in companionship to the compositionality accounted hypothesis testing for the multi-variate microbiome datasets. MarkerML not only helps in identification of marker-features, but also enables insights into the role and inter-dependence of the identified features in driving the decision making of the supervised machine learnt model. Generation of high quality and intuitive visualizations spanning prediction effect plots, model performance reports, feature dependency plots, Shapley and abundance informed cladograms (Sungrams), hypothesis tested violin plots along-with necessary provisions for excluding the participant bias and ensuring reproducibility of results, further seek to make the platform a useful asset for the scientists in the field of microbiome (and even beyond). The MarkerML web-server is freely available for the academic community at https://microbiome.igib.res.in/markerml/.