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Title: Constructing legislative networks in R using incidentally and backbone
Abstract Political network data can often be challenging to collect and clean for analysis. This article demonstrates how the incidentally and backbone packages for R can be used together to construct networks among legislators in the US Congress. These networks can be customized to focus on a specific chamber (Senate or House of Representatives), session (2003 to present), legislation type (bills and resolutions), and policy area (32 topics). Four detailed examples with replicable code are presented to illustrate the types of networks and types of insights that can be obtained using these tools.  more » « less
Award ID(s):
2211744
NSF-PAR ID:
10430769
Author(s) / Creator(s):
Date Published:
Journal Name:
Connections
Volume:
42
Issue:
1
ISSN:
0226-1766
Page Range / eLocation ID:
1 to 9
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. INTRODUCTION Neurons are by far the most diverse of all cell types in animals, to the extent that “cell types” in mammalian brains are still mostly heterogeneous groups, and there is no consensus definition of the term. The Drosophila optic lobes, with approximately 200 well-defined cell types, provides a tractable system with which to address the genetic basis of neuronal type diversity. We previously characterized the distinct developmental gene expression program of each of these types using single-cell RNA sequencing (scRNA-seq), with one-to-one correspondence to the known morphological types. RATIONALE The identity of fly neurons is determined by temporal and spatial patterning mechanisms in stem cell progenitors, but it remained unclear how these cell fate decisions are implemented and maintained in postmitotic neurons. It was proposed in Caenorhabditis elegans that unique combinations of terminal selector transcription factors (TFs) that are continuously expressed in each neuron control nearly all of its type-specific gene expression. This model implies that it should be possible to engineer predictable and complete switches of identity between different neurons just by modifying these sustained TFs. We aimed to test this prediction in the Drosophila visual system. RESULTS Here, we used our developmental scRNA-seq atlases to identify the potential terminal selector genes in all optic lobe neurons. We found unique combinations of, on average, 10 differentially expressed and stably maintained (across all stages of development) TFs in each neuron. Through genetic gain- and loss-of-function experiments in postmitotic neurons, we showed that modifications of these selector codes are sufficient to induce predictable switches of identity between various cell types. Combinations of terminal selectors jointly control both developmental (e.g., morphology) and functional (e.g., neurotransmitters and their receptors) features of neurons. The closely related Transmedullary 1 (Tm1), Tm2, Tm4, and Tm6 neurons (see the figure) share a similar code of terminal selectors, but can be distinguished from each other by three TFs that are continuously and specifically expressed in one of these cell types: Drgx in Tm1, Pdm3 in Tm2, and SoxN in Tm6. We showed that the removal of each of these selectors in these cell types reprograms them to the default Tm4 fate. We validated these conversions using both morphological features and molecular markers. In addition, we performed scRNA-seq to show that ectopic expression of pdm3 in Tm4 and Tm6 neurons converts them to neurons with transcriptomes that are nearly indistinguishable from that of wild-type Tm2 neurons. We also show that Drgx expression in Tm1 neurons is regulated by Klumpfuss, a TF expressed in stem cells that instructs this fate in progenitors, establishing a link between the regulatory programs that specify neuronal fates and those that implement them. We identified an intronic enhancer in the Drgx locus whose chromatin is specifically accessible in Tm1 neurons and in which Klu motifs are enriched. Genomic deletion of this region knocked down Drgx expression specifically in Tm1 neurons, leaving it intact in the other cell types that normally express it. We further validated this concept by demonstrating that ectopic expression of Vsx (visual system homeobox) genes in Mi15 neurons not only converts them morphologically to Dm2 neurons, but also leads to the loss of their aminergic identity. Our results suggest that selector combinations can be further sculpted by receptor tyrosine kinase signaling after neurogenesis, providing a potential mechanism for postmitotic plasticity of neuronal fates. Finally, we combined our transcriptomic datasets with previously generated chromatin accessibility datasets to understand the mechanisms that control brain wiring downstream of terminal selectors. We built predictive computational models of gene regulatory networks using the Inferelator framework. Experimental validations of these networks revealed how selectors interact with ecdysone-responsive TFs to activate a large and specific repertoire of cell surface proteins and other effectors in each neuron at the onset of synapse formation. We showed that these network models can be used to identify downstream effectors that mediate specific cellular decisions during circuit formation. For instance, reduced levels of cut expression in Tm2 neurons, because of its negative regulation by pdm3 , controls the synaptic layer targeting of their axons. Knockdown of cut in Tm1 neurons is sufficient to redirect their axons to the Tm2 layer in the lobula neuropil without affecting other morphological features. CONCLUSION Our results support a model in which neuronal type identity is primarily determined by a relatively simple code of continuously expressed terminal selector TFs in each cell type throughout development. Our results provide a unified framework of how specific fates are initiated and maintained in postmitotic neurons and open new avenues to understanding synaptic specificity through gene regulatory networks. The conservation of this regulatory logic in both C. elegans and Drosophila makes it likely that the terminal selector concept will also be useful in understanding and manipulating the neuronal diversity of mammalian brains. Terminal selectors enable predictive cell fate reprogramming. Tm1, Tm2, Tm4, and Tm6 neurons of the Drosophila visual system share a core set of TFs continuously expressed by each cell type (simplified). The default Tm4 fate is overridden by the expression of a single additional terminal selector to generate Tm1 ( Drgx ), Tm2 ( pdm3 ), or Tm6 ( SoxN ) fates. 
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  2. Abstract

    Species interaction networks, which govern the maintenance of biodiversity and ecosystem processes within ecological communities, are being rapidly altered by anthropogenic activities worldwide. Studies on the response of species interaction networks to anthropogenic disturbance have almost exclusively focused on one interaction type at a time, such as mutualistic or antagonistic interactions, making it challenging to decipher how networks of different interaction types respond to the same anthropogenic disturbance. Moreover, few studies have simultaneously focused on the two main components of network structure: network topology (i.e., architecture) and network ecology (i.e., species identities and interaction turnover), thereby limiting our understanding of the ecological drivers underlying changes in network topology in response to anthropogenic disturbance. Here, we used 16,400 plant–pollinator and plant–herbivore interaction observations from 16 sites along an agricultural intensification gradient to compare changes in network topology and ecology between mutualistic and antagonistic networks. We measured two aspects of network topology—nestedness and modularity—and found that although the mutualistic networks were consistently more nested than antagonistic networks and antagonistic networks were consistently more modular, the rate of change in nestedness and modularity along the gradient was comparable between the two network types. Change in network ecology, however, was distinct between mutualistic and antagonistic networks, with partner switching making a significantly larger contribution to interaction turnover in the mutualistic networks than in the antagonistic networks, and species turnover being a strong contributor to interaction turnover in the antagonistic networks. The ecological and topological changes we observed in the antagonistic and mutualistic networks have different implications for pollinator and herbivore communities in agricultural landscapes, and support the idea that pollinators are more labile in their interaction partner choice, whereas herbivores form more reciprocally specialized, and therefore more vulnerable, interactions. Our results also demonstrate that studying both topological and ecological network structure can help to elucidate the effects of anthropogenic disturbance on ecological communities, with applications for conservation and restoration of species interactions and the ecosystem processes they maintain.

     
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  3. INTRODUCTION A brainwide, synaptic-resolution connectivity map—a connectome—is essential for understanding how the brain generates behavior. However because of technological constraints imaging entire brains with electron microscopy (EM) and reconstructing circuits from such datasets has been challenging. To date, complete connectomes have been mapped for only three organisms, each with several hundred brain neurons: the nematode C. elegans , the larva of the sea squirt Ciona intestinalis , and of the marine annelid Platynereis dumerilii . Synapse-resolution circuit diagrams of larger brains, such as insects, fish, and mammals, have been approached by considering select subregions in isolation. However, neural computations span spatially dispersed but interconnected brain regions, and understanding any one computation requires the complete brain connectome with all its inputs and outputs. RATIONALE We therefore generated a connectome of an entire brain of a small insect, the larva of the fruit fly, Drosophila melanogaster. This animal displays a rich behavioral repertoire, including learning, value computation, and action selection, and shares homologous brain structures with adult Drosophila and larger insects. Powerful genetic tools are available for selective manipulation or recording of individual neuron types. In this tractable model system, hypotheses about the functional roles of specific neurons and circuit motifs revealed by the connectome can therefore be readily tested. RESULTS The complete synaptic-resolution connectome of the Drosophila larval brain comprises 3016 neurons and 548,000 synapses. We performed a detailed analysis of the brain circuit architecture, including connection and neuron types, network hubs, and circuit motifs. Most of the brain’s in-out hubs (73%) were postsynaptic to the learning center or presynaptic to the dopaminergic neurons that drive learning. We used graph spectral embedding to hierarchically cluster neurons based on synaptic connectivity into 93 neuron types, which were internally consistent based on other features, such as morphology and function. We developed an algorithm to track brainwide signal propagation across polysynaptic pathways and analyzed feedforward (from sensory to output) and feedback pathways, multisensory integration, and cross-hemisphere interactions. We found extensive multisensory integration throughout the brain and multiple interconnected pathways of varying depths from sensory neurons to output neurons forming a distributed processing network. The brain had a highly recurrent architecture, with 41% of neurons receiving long-range recurrent input. However, recurrence was not evenly distributed and was especially high in areas implicated in learning and action selection. Dopaminergic neurons that drive learning are amongst the most recurrent neurons in the brain. Many contralateral neurons, which projected across brain hemispheres, were in-out hubs and synapsed onto each other, facilitating extensive interhemispheric communication. We also analyzed interactions between the brain and nerve cord. We found that descending neurons targeted a small fraction of premotor elements that could play important roles in switching between locomotor states. A subset of descending neurons targeted low-order post-sensory interneurons likely modulating sensory processing. CONCLUSION The complete brain connectome of the Drosophila larva will be a lasting reference study, providing a basis for a multitude of theoretical and experimental studies of brain function. The approach and computational tools generated in this study will facilitate the analysis of future connectomes. Although the details of brain organization differ across the animal kingdom, many circuit architectures are conserved. As more brain connectomes of other organisms are mapped in the future, comparisons between them will reveal both common and therefore potentially optimal circuit architectures, as well as the idiosyncratic ones that underlie behavioral differences between organisms. Some of the architectural features observed in the Drosophila larval brain, including multilayer shortcuts and prominent nested recurrent loops, are found in state-of-the-art artificial neural networks, where they can compensate for a lack of network depth and support arbitrary, task-dependent computations. Such features could therefore increase the brain’s computational capacity, overcoming physiological constraints on the number of neurons. Future analysis of similarities and differences between brains and artificial neural networks may help in understanding brain computational principles and perhaps inspire new machine learning architectures. The connectome of the Drosophila larval brain. The morphologies of all brain neurons, reconstructed from a synapse-resolution EM volume, and the synaptic connectivity matrix of an entire brain. This connectivity information was used to hierarchically cluster all brains into 93 cell types, which were internally consistent based on morphology and known function. 
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    Accurate population counts are essential for understanding the status of species and for researchers studying various phenomena including monitoring the relationship between environmental stresses and the spread of disease within populations. Both small roosts and large colonies of bats provide challenges when attempting to determine an accurate population count. Recently, there have been a number of new video analysis software applications, that are available on the internet, which can be used to provide population counts. When software-based counts are compared with manual counts, the software provides counts that are substantially less labor intensive, determined substantially more quickly, and have the potential to be more accurate. This short paper discusses the use of neural networks to determine the number of bats that there are in a region when multiple bats may overlap. The work discussed in this manuscript demonstrates that the counts of multiple overlapping bats can be improved using trained neural networks. This is a critical improvement for providing accurate counts in high density videos. This manuscript contains the biological motivations, and a brief overview of how artificial intelligence is being implemented. The results discussed compare the accuracy values of neural networks for a few case studies including cross-comparisons of data trained on different video types and for different animals which can have accuracy values above 90 % for comparable video types. Finally, the generation and use of synthetic images, to increase the amount of data in a training set, is also discussed, which resulted in a trained neural network that produced an accuracy value of 80% on 12 unbiased categories. 
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  5. Abstract Background

    Inference of person-to-person transmission networks using surveillance data is increasingly used to estimate spatiotemporal patterns of pathogen transmission. Several data types can be used to inform transmission network inferences, yet the sensitivity of those inferences to different data types is not routinely evaluated.

    Methods

    The influence of different combinations of spatial, temporal, and travel-history data on transmission network inferences forPlasmodium falciparummalaria were evaluated.

    Results

    The information content of these data types may be limited for inferring person-to-person transmission networks and may lead to an overestimate of transmission. Only when outbreaks were temporally focal or travel histories were accurate was the algorithm able to accurately estimate the reproduction number under control,Rc. Applying this approach to data from Eswatini indicated that inferences ofRcand spatiotemporal patterns therein depend upon the choice of data types and assumptions about travel-history data.

    Conclusions

    These results suggest that transmission network inferences made with routine malaria surveillance data should be interpreted with caution.

     
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