Rapid evolutionary change over a few generations has been documented in natural populations. Such changes are observed as organisms invade new environments, and they are often triggered by changed interspecific interactions, such as differences in predation regimes. However, in spite of increased recognition of antagonistic male-female mating interactions, there is very limited evidence that such intraspecific interactions could cause rapid evolutionary dynamics in nature. This is because ecological and longitudinal data from natural populations have been lacking. Here we show that in a color-polymorphic damselfly species, male-female mating interactions lead to rapid evolutionary change in morph frequencies between generations. Field data and computer simulations indicate that these changes are driven by sexual conflict, in which morph fecundities are negatively affected by frequency-and density-dependent male mating harassment. These frequency-dependent processes prevent population divergence by maintaining a female polymorphism in most populations. Although these results contrast with the traditional view of how sexual conflict enhances the rate of population divergence, they are consistent with a recent theoretical model of how females may form discrete genetic clusters in response to male mating harassment.

Although predation is thought to affect species divergence, the effects of predator-mediated natural selection on species divergence and in nonadaptive radiations have seldom been studied. Wing melanization in Calopteryx damselflies has important functions in sexual selection and interspecific interactions and in species recognition. The genus Calopteryx and other damselfly genera have also been put forward as examples of radiations driven by sexual selection. We show that avian predation strongly affects natural selection on wing morphology and male wing melanization in two congeneric and sympatric species of this genus (Calopteryx splendens and Calopteryx virgo). Predation risk was almost three times higher for C. virgo, which has an exaggerated degree of wing melanization, than it was for the less exaggerated, sympatric congener C. splendens. Selective predation on the exaggerated species C. virgo favored a reduction and redistribution of the wing melanin patch. There was evidence for nonlinear selection involving wing patch size, wing patch darkness, and wing length and width in C. splendens but weaker nonlinear selection on the same trait combinations in C. virgo. Selective predation could interfere with species divergence by sexual selection and may thus indirectly affect male interspecific interactions, reproductive isolation, and species coexistence in this genus.

Probabilistic models have provided the underpinnings for state-of-the-art performance in many single-cell omics data analysis tasks, including dimensionality reduction, clustering, differential expression, annotation, removal of unwanted variation, and integration across modalities. Many of the models being deployed are amenable to scalable stochastic inference techniques, and accordingly they are able to process single-cell datasets of realistic and growing sizes. However, the community-wide adoption of probabilistic approaches is hindered by a fractured software ecosystem resulting in an array of packages with distinct, and often complex interfaces. To address this issue, we developed scvi-tools (https://scvi-tools.org), a Python package that implements a variety of leading probabilistic methods. These methods, which cover many fundamental analysis tasks, are accessible through a standardized, easy-to-use interface with direct links to Scanpy, Seurat, and Bioconductor workflows. By standardizing the implementations, we were able to develop and reuse novel functionalities across different models, such as support for complex study designs through nonlinear removal of unwanted variation due to multiple covariates and reference-query integration via scArches. The extensible software building blocks that underlie scvi-tools also enable a developer environment in which new probabilistic models for single cell omics can be efficiently developed, benchmarked, and deployed. We demonstrate this through a code-efficient reimplementation of Stereoscope for deconvolution of spatial transcriptomics profiles. By catering to both the end user and developer audiences, we expect scvi-tools to become an essential software dependency and serve to formulate a community standard for probabilistic modeling of single cell omics.

We introduce FlexCore, the first exemplar of an architecture based on the FlexSoC framework. Comprising the same datapath units found in a conventional five-stage pipeline, the FlexCore has an exposed datapath control and a flexible interconnect to allow the datapath to be dynamically reconfigured as a consequence of code generation. Additionally, the FlexCore allows specialized datapath units to be inserted and utilized within the same architecture and compilation framework.This study shows that, in comparison to a conventional fivestage general-purpose processor, the FlexCore is up to 40% more efficient in terms of cycle count on a set of benchmarks from the embedded application domain. We show that both the finegrained control and the flexible interconnect contribute to the speedup. Furthermore, according to our VLSI implementation study, the FlexCore architecture offers both time and energy savings.The exposed FlexCore datapath requires a wide control word. The conducted evaluation confirms that this increases the instruction bandwidth and memory footprint. This calls for efficient instruction decoding as proposed in the FlexSoC framework.

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