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Bacterial pathogens pose a major risk to human health, leading to tens of millions of deaths annually and significant global economic losses. While bacterial infections are typically treated with antibiotic regimens, there has been a rapid emergence of antimicrobial resistant (AMR) bacterial strains due to antibiotic overuse. Because of this, treatment of infections with traditional antimicrobials has become increasingly difficult, necessitating the development of innovative approaches for deeply understanding pathogen function. To combat issues presented by broad- spectrum antibiotics, the idea of narrow-spectrum antibiotics has been previously proposed and explored. Rather than interrupting universal bacterial cellular processes, narrow-spectrum antibiotics work by targeting specific functions or essential genes in certain species or subgroups of bacteria. Here, we generate a collection of genome-scale metabolic network reconstructions (GENREs) of pathogens through an automated computational pipeline. We used these GENREs to identify subgroups of pathogens that share unique metabolic phenotypes and determined that pathogen physiological niche plays a role in the development of unique metabolic function. For example, we identified several unique metabolic phenotypes specific to stomach pathogens. We identified essential genes unique to stomach pathogens in silico and a corresponding inhibitory compound for a uniquely essential gene. We then validated our in silico predictions with an in vitro microbial growth assay. We demonstrated that the inhibition of a uniquely essential gene,thyX, inhibited growth of stomach-specific pathogens exclusively, indicating possible physiological location-specific targeting. This pioneering computational approach could lead to the identification of unique metabolic signatures to inform future targeted, physiological location-specific, antimicrobial therapies, reducing the need for broad-spectrum antibiotics. 

Abstract The seminal result of Benamou and Brenier provides a characterization of the Wasserstein distance as the path of the minimal action in the space of probability measures, where paths are solutions of the continuity equation and the action is the kinetic energy. Here we consider a fundamental modification of the framework where the paths are solutions of nonlocal (jump) continuity equations and the action is a nonlocal kinetic energy. The resulting nonlocal Wasserstein distances are relevant to fractional diffusions and Wasserstein distances on graphs. We characterize the basic properties of the distance and obtain sharp conditions on the (jump) kernel specifying the nonlocal transport that determine whether the topology metrized is the weak or the strong topology. A key result of the paper are the quantitative comparisons between the nonlocal and local Wasserstein distance. 

Disease surveillance systems provide early warnings of disease outbreaks before they become public health emergencies. However, pandemics containment would be challenging due to the complex immunity landscape created by multiple variants. Genomic surveillance is critical for detecting novel variants with diverse characteristics and importation/emergence times. Yet, a systematic study incorporating genomic monitoring, situation assessment, and intervention strategies is lacking in the literature. We formulate an integrated computational modeling framework to study a realistic course of action based on sequencing, analysis, and response. We study the effects of the second variant’s importation time, its infectiousness advantage and, its cross-infection on the novel variant’s detection time, and the resulting intervention scenarios to contain epidemics driven by two-variants dynamics. Our results illustrate the limitation in the intervention’s effectiveness due to the variants’ competing dynamics and provide the following insights: i) There is a set of importation times that yields the worst detection time for the second variant, which depends on the first variant’s basic reproductive number; ii) When the second variant is imported relatively early with respect to the first variant, the cross-infection level does not impact the detection time of the second variant. We found that depending on the target metric, the best outcomes are attained under different interventions’ regimes. Our results emphasize the importance of sustained enforcement of Non-Pharmaceutical Interventions on preventing epidemic resurgence due to importation/emergence of novel variants. We also discuss how our methods can be used to study when a novel variant emerges within a population. 

An ancient, trans-specific polymorphism provides insights into the evolution of life history strategies. 

While surface microstructures of butterfly wings have been extensively studied for their structural coloration or optical properties within the visible spectrum, their properties in infrared wavelengths with potential ties to thermoregulation are relatively unknown. The midinfrared wavelengths of 7.5 to 14 µm are particularly important for radiative heat transfer in the ambient environment, because of the overlap with the atmospheric transmission window. For instance, a high midinfrared emissivity can facilitate surface cooling, whereas a low midinfrared emissivity can minimize heat loss to surroundings. Here we find that the midinfrared emissivity of butterfly wings from warmer climates such asArchaeoprepona demophoon(Oaxaca, Mexico) andHeliconius sara(Pichincha, Ecuador) is up to 2 times higher than that of butterfly wings from cooler climates such asCelastrina echo(Colorado) andLimenitis arthemis(Florida), using Fourier-transform infrared (FTIR) spectroscopy and infrared thermography. Our optical computations using a unit cell approach reproduce the spectroscopy data and explain how periodic microstructures play a critical role in the midinfrared. The emissivity spectrum governs the temperature of butterfly wings, and we demonstrate thatC. echowings heat up to 8 °C more thanA. demophoonwings under the same sunlight in the clear sky of Irvine, CA. Furthermore, our thermal computations show that butterfly wings in their respective habitats can maintain a moderate temperature range through a balance of solar absorption and infrared emission. These findings suggest that the surface microstructures of butterfly wings potentially contribute to thermoregulation and provide an insight into butterflies' survival. 

Primary biodiversity data records that are open access and available in a standardised format are essential for conservation planning and research on policy-relevant time-scales. We created a dataset to document all known occurrence data for the Federally Endangered Poweshiek skipperling butterfly [ Oarismapoweshiek (Parker, 1870; Lepidoptera: Hesperiidae)]. The Poweshiek skipperling was a historically common species in prairie systems across the upper Midwest, United States and Manitoba, Canada. Rapid declines have reduced the number of verified extant sites to six. Aggregating and curating Poweshiek skipperling occurrence records documents and preserves all known distributional data, which can be used to address questions related to Poweshiek skipperling conservation, ecology and biogeography. Over 3500 occurrence records were aggregated over a temporal coverage from 1872 to present. Occurrence records were obtained from 37 data providers in the conservation and natural history collection community using both “HumanObservation” and “PreservedSpecimen” as an acceptable basisOfRecord. Data were obtained in different formats and with differing degrees of quality control. During the data aggregation and cleaning process, we transcribed specimen label data, georeferenced occurrences, adopted a controlled vocabulary, removed duplicates and standardised formatting. We examined the dataset for inconsistencies with known Poweshiek skipperling biogeography and phenology and we verified or removed inconsistencies by working with the original data providers. In total, 12 occurrence records were removed because we identified them to be the western congener Oarismagarita (Reakirt, 1866). This resulting dataset enhances the permanency of Poweshiek skipperling occurrence data in a standardised format. This is a validated and comprehensive dataset of occurrence records for the Poweshiek skipperling ( Oarismapoweshiek ) utilising both observation and specimen-based records. Occurrence data are preserved and available for continued research and conservation projects using standardised Darwin Core formatting where possible. Prior to this project, much of these occurrence records were not mobilised and were being stored in individual institutional databases, researcher datasets and personal records. This dataset aggregates presence data from state conservation agencies, natural heritage programmes, natural history collections, citizen scientists, researchers and the U.S. Fish & Wildlife Service. The data include opportunistic observations and collections, research vouchers, observations collected for population monitoring and observations collected using standardised research methodologies. The aggregated occurrence records underwent cleaning efforts that improved data interoperablitity, removed transcription errors and verified or removed uncertain data. This dataset enhances available information on the spatiotemporal distribution of this Federally Endangered species. As part of this aggregation process, we discovered and verified Poweshiek skipperling occurrence records from two previously unknown states, Nebraska and Ohio. 

Abstract Butterflies are a diverse and charismatic insect group that are thought to have evolved with plants and dispersed throughout the world in response to key geological events. However, these hypotheses have not been extensively tested because a comprehensive phylogenetic framework and datasets for butterfly larval hosts and global distributions are lacking. We sequenced 391 genes from nearly 2,300 butterfly species, sampled from 90 countries and 28 specimen collections, to reconstruct a new phylogenomic tree of butterflies representing 92% of all genera. Our phylogeny has strong support for nearly all nodes and demonstrates that at least 36 butterfly tribes require reclassification. Divergence time analyses imply an origin ~100 million years ago for butterflies and indicate that all but one family were present before the K/Pg extinction event. We aggregated larval host datasets and global distribution records and found that butterflies are likely to have first fed on Fabaceae and originated in what is now the Americas. Soon after the Cretaceous Thermal Maximum, butterflies crossed Beringia and diversified in the Palaeotropics. Our results also reveal that most butterfly species are specialists that feed on only one larval host plant family. However, generalist butterflies that consume two or more plant families usually feed on closely related plants. 

Abstract Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.