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Abstract Pan-genomics and genome-editing technologies are revolutionizing breeding of global crops^1,2. A transformative opportunity lies in exchanging genotype-to-phenotype knowledge between major crops (that is, those cultivated globally) and indigenous crops (that is, those locally cultivated within a circumscribed area)^3–5to enhance our food system. However, species-specific genetic variants and their interactions with desirable natural or engineered mutations pose barriers to achieving predictable phenotypic effects, even between related crops^6,7. Here, by establishing a pan-genome of the crop-rich genusSolanum⁸and integrating functional genomics and pan-genetics, we show that gene duplication and subsequent paralogue diversification are major obstacles to genotype-to-phenotype predictability. Despite broad conservation of gene macrosynteny among chromosome-scale references for 22 species, including 13 indigenous crops, thousands of gene duplications, particularly within key domestication gene families, exhibited dynamic trajectories in sequence, expression and function. By augmenting our pan-genome with African eggplant cultivars⁹and applying quantitative genetics and genome editing, we dissected an intricate history of paralogue evolution affecting fruit size. The loss of a redundant paralogue of the classical fruit size regulatorCLAVATA3(CLV3)^10,11was compensated by a lineage-specific tandem duplication. Subsequent pseudogenization of the derived copy, followed by a large cultivar-specific deletion, created a single fusedCLV3allele that modulates fruit organ number alongside an enzymatic gene controlling the same trait. Our findings demonstrate that paralogue diversifications over short timescales are underexplored contingencies in trait evolvability. Exposing and navigating these contingencies is crucial for translating genotype-to-phenotype relationships across species. 

Video Conferencing Applications (VCAs) that support remote work and education have increased in use over the last two years, contributing to Internet bandwidth usage. VCA clients transmit video and audio to each other in peer-to-peer mode or through a bridge known as a Selective Forwarding Unit (SFU). Popular VCAs implement congestion control in the application layer over UDP and accomplish rate adjustment through video rate control, ultimately affecting end user Quality of Experience(QoE). Researchers have reported on the throughput and video metric performance of specific VCAs using structuredexperiments. Yet prior work rarely examines the interaction between congestion control mechanisms and rate adjustment techniques that produces the observed throughput and QoE metrics. Understanding this interaction at a functional level paves the way to explain observed performance, to pinpoint commonalities and key functional differences across VCAs, and to contemplate opportunities for innovation. To that end, we first design and conduct detailed measurements of three VCAs(WebRTC/Jitsi, Zoom, Blue Jeans) to develop understanding of their congestion and video rate control mechanisms. We then use the measurement results to derive our functional models for the VCA client and SFU. Our models reveal the complexity of these systems and demonstrate how, despite some uniformity in function deployment, there is significant variability among the VCAs in the implementation of these functions. 

Abstract Microbially Induced Desaturation and Precipitation (MIDP) through denitrification is an emerging ground improvement method in which indigenous nitrate reducing bacteria are stimulated to introduce biogas, biominerals and biomass in the soil matrix. In this study, a numerical model is developed to evaluate the effect of biogas, biominerals and biomass on the hydraulic properties of soils treated with MIDP. The proposed model couples the biochemical conversions to changes of porosity and water saturation and predicts changes in permeability through two separate power law equations. Experimental studies from the literature are used to calibrate the model. Comparing the results with other studies on bioclogging or biomineralization in porous media reveals that the combined production of biogas, biomass, and biominerals results in efficient clogging, in the sense that only a small amount of products leads to a substantial permeability reduction. Based on this comparison, the authors postulate that biogenic gas bubbles preferably form within the larger pore bodies. The presence of biogenic gas in the larger pore bodies forces calcium carbonate minerals and biomass to be formed mainly at the pore throats. The interaction between the different phases results in more efficient clogging than observed in other studies which focus on a single product only. 

An enduring question in evolutionary biology concerns the degree to which episodes of convergent trait evolution depend on the same genetic programs, particularly over long timescales. In this work, we genetically dissected repeated origins and losses of prickles—sharp epidermal projections—that convergently evolved in numerous plant lineages. Mutations in a cytokinin hormone biosynthetic gene caused at least 16 independent losses of prickles in eggplants and wild relatives in the genusSolanum. Homologs underlie prickle formation across angiosperms that collectively diverged more than 150 million years ago, including rice and roses. By developing newSolanumgenetic systems, we leveraged this discovery to eliminate prickles in a wild species and an indigenously foraged berry. Our findings implicate a shared hormone activation genetic program underlying evolutionarily widespread and recurrent instances of plant morphological innovation. 

In machine learning, we often face the situation where the event we are interested in has very few data points buried in a massive amount of data. This is typical in network monitoring, where data are streamed from sensing or measuring units continuously but most data are not for events. With imbalanced datasets, the classifiers tend to be biased in favor of the main class. Rare event detection has received much attention in machine learning, and yet it is still a challenging problem. In this paper, we propose a remedy for the standing problem. Weighting and sampling are two fundamental approaches to address the problem. We focus on the weighting method in this paper. We first propose a boosting-style algorithm to compute class weights, which is proved to have excellent theoretical property. Then we propose an adaptive algorithm, which is suitable for real-time applications. The adaptive nature of the two algorithms allows a controlled tradeoff between true positive rate and false positive rate and avoids excessive weight on the rare class, which leads to poor performance on the main class. Experiments on power grid data and some public datasets show that the proposed algorithms outperform the existing weighting and boosting methods, and that their superiority is more noticeable with noisy data. 

Abstract The highly diverse Solanaceae family contains several widely studied models and crop species. Fully exploring, appreciating, and exploiting this diversity requires additional model systems. Particularly promising are orphan fruit crops in the genus Physalis, which occupy a key evolutionary position in the Solanaceae and capture understudied variation in traits such as inflorescence complexity, fruit ripening and metabolites, disease and insect resistance, self-compatibility, and most notable, the striking inflated calyx syndrome (ICS), an evolutionary novelty found across angiosperms where sepals grow exceptionally large to encapsulate fruits in a protective husk. We recently developed transformation and genome editing in Physalis grisea (groundcherry). However, to systematically explore and unlock the potential of this and related Physalis as genetic systems, high-quality genome assemblies are needed. Here, we present chromosome-scale references for P. grisea and its close relative Physalis pruinosa and use these resources to study natural and engineered variations in floral traits. We first rapidly identified a natural structural variant in a bHLH gene that causes petal color variation. Further, and against expectations, we found that CRISPR–Cas9-targeted mutagenesis of 11 MADS-box genes, including purported essential regulators of ICS, had no effect on inflation. In a forward genetics screen, we identified huskless, which lacks ICS due to mutation of an AP2-like gene that causes sepals and petals to merge into a single whorl of mixed identity. These resources and findings elevate Physalis to a new Solanaceae model system and establish a paradigm in the search for factors driving ICS.