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Ciliates are a model lineage for studies of genome architecture given their unusual genome structures. All ciliates have both somatic macronuclei (MAC) and germline micronuclei (MIC), both of which develop from a zygotic nucleus following sex (i.e., conjugation). Nuclear developmental stages are not well documented among non-model ciliates, includingChilodonella uncinata(class Phyllopharyngea), the focus of our work. Here, we characterize nuclear architecture and genome dynamics inC. uncinataby combining 4′,6-diamidino-2-phenylindole (DAPI) staining and fluorescencein situhybridization (FISH) techniques with confocal microscopy. We developed a telomere probe for staining, which alongside DAPI allows for the identification of fragmented somatic chromosomes among the total DNA in the nuclei. We quantify both total DNA and telomere-bound signals from more than 250 nuclei sampled from 116 individual cells, and analyze changes in DNA content and nuclear architecture acrossChilodonella’s nuclear life cycle. Specifically, we find that MAC developmental stages in the ciliateC. uncinataare different from those reported from other ciliate species. These data provide insights into nuclear dynamics during development and enrich our understanding of genome evolution in non-model ciliates. IMPORTANCECiliates are a clade of diverse single-celled eukaryotic microorganisms that contain at least one somatic macronucleus (MAC) and germline micronucleus (MIC) within each cell/organism. Ciliates rely on complex genome rearrangements to generate somatic genomes from a zygotic nucleus. However, the development of somatic nuclei has only been documented for a few model ciliate genera, includingParamecium,Tetrahymena, andOxytricha. Here, we study the MAC developmental process in the non-model ciliate,C. uncinata. We analyze both total DNA and the generation of gene-sized somatic chromosomes using a laser scanning confocal microscope to describeC. uncinata’s nuclear life cycle. We show that DNA content changes dramatically during their life cycle and in a manner that differs from previous studies on model ciliates. Our study expands knowledge of genome dynamics in ciliates and among eukaryotes more broadly. 

In real-world phenomena which involve mutual influence or causal effects between interconnected units, equilibrium states are typically represented with cycles in graphical models. An expressive class of graphical models, relational causal models, can represent and reason about complex dynamic systems exhibiting such cycles or feedback loops. Existing cyclic causal discovery algorithms for learning causal models from observational data assume that the data instances are independent and identically distributed which makes them unsuitable for relational causal models. At the same time, causal discovery algorithms for relational causal models assume acyclicity. In this work, we examine the necessary and sufficient conditions under which a constraint-based relational causal discovery algorithm is sound and complete for cyclic relational causal models. We introduce relational acyclification, an operation specifically designed for relational models that enables reasoning about the identifiability of cyclic relational causal models. We show that under the assumptions of relational acyclification and sigma-faithfulness, the relational causal discovery algorithm RCD is sound and complete for cyclic relational models. We present experimental results to support our claim. 

The success of artificial neural networks (ANNs) in machine vision techniques has driven hardware researchers to explore more efficient computing elements for energy-expensive operations such as vector-matrix multiplication (VMM). In this work, InP-based floating-gate photo-field-effective transistors (FG-PFETs) are demonstrated as computing elements that integrate both photodetection and initial signal processing at the sensor level. These devices are fabricated from semiconductor channels grown via a back-end CMOS compatible templated liquid phase (TLP) approach. Individual devices are shown to exhibit programmable responsivity, mimicking the effect of a synapse connecting the photodetector to a neuron. Using these devices, a simulated optical neural network (ONN) where the experimentally measured performance of FG-PFETs is used as an input shows excellent image recognition accuracy for color-mixed handwritten digits.