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Over past 20 years programming contests have grown substantial expertise in code assessment, yet it is rarely applied outside the contest settings. This paper presents a case of successful adoption of Olympiad-style code assessment technology in Codility, a commercial web service intended for pre-hire screening of software developers. We discuss correctness and soundness of the automated pre-hire screening, and characterise the class of programming tasks best suited for such application. [ABSTRACT FROM AUTHOR]

Spirobacillus cienkowskii ( Spirobacillus, hereafter) is a widely distributed bacterial pathogen that has significant impacts on the population dynamics of zooplankton ( Daphnia spp .) , particularly in months when Daphnia are asexually reproducing. However, little is known about Spirobacillus' virulence, transmission mode, and dynamics. As a result, we cannot explain the dynamics of Spirobacillus epidemics in nature or use Spirobacillus as a model pathogen, despite Daphnia's tractability as a model host. Here, we work to fill these knowledge gaps experimentally. We found that Spirobacillus is among the most virulent of Daphnia pathogens, killing its host within a week and reducing host fecundity. We further found that Spirobacillus did not transmit horizontally among hosts unless the host died or was destroyed (i.e., it is an "obligate killer"). In experiments aimed at quantifying the dynamics of horizontal transmission among asexually reproducing Daphnia , we demonstrated that Spirobacillus transmits poorly in the laboratory. In mesocosms, Spirobacillus failed to generate epidemics; in experiments wherein individual Daphnia were exposed, Spirobacillus' transmission success was low. In the (limited) set of conditions we considered, Spirobacillus' transmission success did not change with host density or pathogen dose and declined following environmental incubation. Finally, we conducted a field survey of Spirobacillus' prevalence within egg cases (ephippia) made by sexually reproducing Daphnia . We found Spirobacillus DNA in ~40% of ephippia, suggesting that, in addition to transmitting horizontally among asexually reproducing Daphnia , Spirobacillus may transmit vertically from sexually reproducing Daphnia . Our work fills critical gaps in the biology of Spirobacillus and illuminates new hypotheses vis-à-vis its life history., Importance: Spirobacillus cienkowskii is a bacterial pathogen of zooplankton, first described in the 19 th century and recently placed in a new family of bacteria, the Silvanigrellaceae . Spirobacillus causes large epidemics in lake zooplankton populations and increases the probability that zooplankton will be eaten by predators. However, little is known about how Spirobacillus transmits among hosts, to what extent it reduces host survival and reproduction (i.e., how virulent it is), and what role virulence plays in Spirobacillus ' life cycle. Here, we experimentally quantified Spirobacillus' virulence and showed that Spirobacillus must kill its host to transmit horizontally. We also found evidence that Spirobacillus may transmit vertically via Daphnia' s seed-like egg sacks. Our work will help scientists to (i) understand Spirobacillus epidemics, (ii) use Spirobacillus as a model pathogen for the study of host-parasite interactions, and (iii) better understand the unusual group of bacteria to which Spirobacillus belongs., Competing Interests: The authors declare no conflict of interest.

For decades, mathematical models have been used to understand the course and outcome of malaria infections (i.e., infection dynamics) and the evolutionary dynamics of the parasites that cause them. A key conclusion of these models is that red blood cell (RBC) availability is a fundamental driver of infection dynamics and parasite trait evolution. The extent to which this conclusion holds will in part depend on model assumptions about the host-mediated processes that regulate RBC availability i.e., removal of uninfected RBCs and supply of RBCs. Diverse mathematical functions have been used to describe host-mediated RBC supply and clearance, but it remains unclear whether they adequately capture the dynamics of RBC supply and clearance during infection. Here, we use a unique dataset, comprising time-series measurements of erythrocyte (i.e., mature RBC) and reticulocyte (i.e., newly supplied RBC) densities during Plasmodium chabaudi malaria infection, and a quantitative data-transformation scheme to elucidate whether RBC dynamics conform to common model assumptions. We found that RBC clearance and supply are not well described by mathematical functions commonly used to model these processes. Furthermore, the temporal dynamics of both processes vary with parasite growth rate in a manner again not captured by existing models. Together, these finding suggest that new model formulations are required if we are to explain and ultimately predict the within-host population dynamics and evolution of malaria parasites.

Nina Wale works in the field of infectious disease evolution and ecology. In this mSphere of Influence article, she reflects on how the paper by Roller and Schmidt, "The physiology and ecological implications of efficient growth" (B. R. Roller and T. M. Schmidt, ISME J 9:1481-1487, 2015, https://doi.org/10.1038/ismej.2014.235) broadened her thinking about how microbes acquire and allocate resources and, in so doing, set her research on pathogen virulence evolution in a new direction.

Predators can strongly influence disease transmission and evolution, particularly when they prey selectively on infected hosts. Although selective predation has been observed in numerous systems, why predators select infected prey remains poorly understood. Here, we use a mathematical model of predator vision to test a long-standing hypothesis about the mechanistic basis of selective predation in a Daphnia -microparasite system, which serves as a model for the ecology and evolution of infectious diseases. Bluegill sunfish feed selectively on Daphnia infected by a variety of parasites, particularly in water uncolored by dissolved organic carbon. The leading hypothesis for selective predation in this system is that infection-induced changes in the transparency of Daphnia render them more visible to bluegill. Rigorously evaluating this hypothesis requires that we quantify the effect of infection on the visibility of prey from the predator's perspective, rather than our own. Using a model of the bluegill visual system, we show that three common parasites, Metschnikowia bicuspidata , Pasteuria ramosa , and Spirobacillus cienkowskii , decrease the transparency of Daphnia , rendering infected Daphnia darker against a background of bright downwelling light. As a result of this increased brightness contrast, bluegill can see infected Daphnia at greater distances than uninfected Daphnia -between 19% and 33% further, depending on the parasite. Pasteuria and Spirobacillus also increase the chromatic contrast of Daphnia . These findings lend support to the hypothesis that selective predation by fish on infected Daphnia could result from the effects of infection on Daphnia 's visibility. However, contrary to expectations, the visibility of Daphnia was not strongly impacted by water color in our model. Our work demonstrates that models of animal visual systems can be useful in understanding ecological interactions that impact disease transmission., Competing Interests: The authors have no conflicts of interest to declare., (© 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

The relative importance of evolutionary history and ecology for traits that drive ecosystem processes is poorly understood. Consumers are essential drivers of nutrient cycling on coral reefs, and thus ecosystem productivity. We use nine consumer "chemical traits" associated with nutrient cycling, collected from 1,572 individual coral reef fishes (178 species spanning 41 families) in two biogeographic regions, the Caribbean and Polynesia, to quantify the relative importance of phylogenetic history and ecological context as drivers of chemical trait variation on coral reefs. We find: (1) phylogenetic relatedness is the best predictor of all chemical traits, substantially outweighing the importance of ecological factors thought to be key drivers of these traits, (2) phylogenetic conservatism in chemical traits is greater in the Caribbean than Polynesia, where our data suggests that ecological forces have a greater influence on chemical trait variation, and (3) differences in chemical traits between regions can be explained by differences in nutrient limitation associated with the geologic context of our study locations. Our study provides multiple lines of evidence that phylogeny is a critical determinant of contemporary nutrient dynamics on coral reefs. More broadly our findings highlight the utility of evolutionary history to improve prediction in ecosystem ecology., (© 2021. The Author(s).)

AbstractEcologists and evolutionary biologists are fascinated by life's variation but also seek to understand phenomena and mechanisms that apply broadly across taxa. Model systems can help us extract generalities from amid all the wondrous diversity, but only if we choose and develop them carefully, use them wisely, and have a range of model systems from which to choose. In this introduction to the Special Feature on Model Systems in Ecology, Evolution, and Behavior (EEB), we begin by grappling with the question, What is a model system? We then explore where our model systems come from, in terms of the skills and other attributes required to develop them and the historical biases that influence traditional model systems in EEB. We emphasize the importance of communities of scientists in the success of model systems-narrow scientific communities can restrict the model organisms themselves. We also consider how our discipline was built around one type of "model scientist"-a history still reflected in the field. This lack of diversity in EEB is unjust and also narrows the field's perspective, including by restricting the questions asked and talents used to answer them. Increasing diversity, equity, and inclusion will require acting at many levels, including structural changes. Diversity in EEB, in both model systems and the scientists who use them, strengthens our discipline.

AbstractEver since biologists began studying the ecology and evolution of infectious diseases (EEID), laboratory-based model systems have been important for developing and testing theory. Yet what EEID researchers mean by the term "model systems" and what they want from them is unclear. This uncertainty hinders our ability to maximally exploit these systems, identify knowledge gaps, and establish effective new model systems. Here, we borrow a definition of model systems from the biomolecular sciences to assess how EEID researchers are (and are not) using 10 key model systems. According to this definition, model systems in EEID are not being used to their fullest and, in fact, cannot even be considered model systems. Research using these systems consistently addresses only two of the three fundamental processes that underlie disease dynamics-transmission and disease, but not recovery. Furthermore, studies tend to focus on only a few scales of biological organization that matter for disease ecology and evolution. Moreover, the field lacks an infrastructure to perform comparative analyses. We aim to begin a discussion of what we want from model systems, which would further progress toward a thorough, holistic understanding of EEID.

Lay Summary: Competition often occurs among diverse parasites within a single host, but control efforts could change its strength. We examined how the interplay between competition and control could shape the evolution of parasite traits like drug resistance and disease severity., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Foundation for Evolution, Medicine, and Public Health.)

Hosts defend themselves against pathogens by mounting an immune response. Fully understanding the immune response as a driver of host disease and pathogen evolution requires a quantitative account of its impact on parasite population dynamics. Here, we use a data-driven modeling approach to quantify the birth and death processes underlying the dynamics of infections of the rodent malaria parasite, Plasmodium chabaudi , and the red blood cells (RBCs) it targets. We decompose the immune response into 3 components, each with a distinct effect on parasite and RBC vital rates, and quantify the relative contribution of each component to host disease and parasite density. Our analysis suggests that these components are deployed in a coordinated fashion to realize distinct resource-directed defense strategies that complement the killing of parasitized cells. Early in the infection, the host deploys a strategy reminiscent of siege and scorched-earth tactics, in which it both destroys RBCs and restricts their supply. Late in the infection, a "juvenilization" strategy, in which turnover of RBCs is accelerated, allows the host to recover from anemia while holding parasite proliferation at bay. By quantifying the impact of immunity on both parasite fitness and host disease, we reveal that phenomena often interpreted as immunopathology may in fact be beneficial to the host. Finally, we show that, across mice, the components of the host response are consistently related to each other, even when infections take qualitatively different trajectories. This suggests the existence of simple rules that govern the immune system's deployment., Competing Interests: The authors declare no competing interest., (Copyright © 2019 the Author(s). Published by PNAS.)

We report here the near-complete genome sequence of " Candidatus Spirobacillus cienkowskii," a spiral-shaped, red-pigmented uncultivated bacterial pathogen of Daphnia spp. The genome is 2.74 Mbp in size, has a GC content of 32.1%, and contains genes associated with bacterial motility and the production of carotenoids, which could explain the distinctive red color of hosts infected with this pathogen.

Slowing the evolution of antimicrobial resistance is essential if we are to continue to successfully treat infectious diseases. Whether a drug-resistant mutant grows to high densities, and so sickens the patient and spreads to new hosts, is determined by the competitive interactions it has with drug-susceptible pathogens within the host. Competitive interactions thus represent a good target for resistance management strategies. Using an in vivo model of malaria infection, we show that limiting a resource that is disproportionately required by resistant parasites retards the evolution of drug resistance by intensifying competitive interactions between susceptible and resistant parasites. Resource limitation prevented resistance emergence regardless of whether resistant mutants arose de novo or were experimentally added before drug treatment. Our work provides proof of principle that chemotherapy paired with an "ecological" intervention can slow the evolution of resistance to antimicrobial drugs, even when resistant pathogens are present at high frequencies. It also suggests that a broad range of previously untapped compounds could be used for treating infectious diseases., Competing Interests: The authors declare no conflict of interest., (Copyright © 2017 the Author(s). Published by PNAS.)

Hosts are often infected with multiple strains of a single parasite species. Within-host competition between parasite strains can be intense and has implications for the evolution of traits that impact patient health, such as drug resistance and virulence. Yet the mechanistic basis of within-host competition is poorly understood. Here, we demonstrate that a parasite nutrient, para-aminobenzoic acid (pABA), mediates competition between a drug resistant and drug susceptible strain of the malaria parasite, Plasmodium chabaudi We further show that increasing pABA supply to hosts infected with the resistant strain worsens disease and changes the relationship between parasite burden and pathology. Our experiments demonstrate that, even when there is profound top-down regulation (immunity), bottom-up regulation of pathogen populations can occur and that its importance may vary during an infection. The identification of resources that can be experimentally controlled opens up the opportunity to manipulate competitive interactions between parasites and hence their evolution., (© 2017 The Authors.)

The evolution of resistance to antimicrobial chemotherapy is a major and growing cause of human mortality and morbidity. Comparatively little attention has been paid to how different patient treatment strategies shape the evolution of resistance. In particular, it is not clear whether treating individual patients aggressively with high drug dosages and long treatment durations, or moderately with low dosages and short durations can better prevent the evolution and spread of drug resistance. Here, we summarize the very limited available empirical evidence across different pathogens and provide a conceptual framework describing the information required to effectively manage drug pressure to minimize resistance evolution., (© 2014 The Author(s) Published by the Royal Society. All rights reserved.)

Motivation: Networks to predict protein pharmacology can be created using ligand similarity or using known bioassay response profiles of ligands. Recent publications indicate that similarity methods can be highly accurate, but it has been unclear how similarity methods compare to methods that use bioassay response data directly., Results: We created protein networks based on ligand similarity (Similarity Ensemble Approach or SEA) and ligand bioassay response-data (BARD) using 155 Pfizer internal BioPrint assays. Both SEA and BARD successfully cluster together proteins with known relationships, and predict some non-obvious relationships. Although the approaches assess target relations from different perspectives, their networks overlap considerably (40% overlap of the top 2% of correlated edges). They can thus be considered as comparable methods, with a distinct advantage of the similarity methods that they only require simple computations (similarity of compound) as opposed to extensive experimental data., Contacts: djwild@indiana.edu; eric.gifford@pfizer.com., Supplementary Information: Supplementary data are available at Bioinformatics online.

The aim of this manuscript is to describe how modern advances in our knowledge of viruses and viral evolution can be applied to the fields of disease ecology and conservation. We review recent progress in virology and provide examples of how it is informing both empirical research in field ecology and applied conservation. We include a discussion of needed breakthroughs and ways to bridge communication gaps between the field and the lab. In an effort to foster this interdisciplinary effort, we have also included a table that lists the definitions of key terms. The importance of understanding the dynamics of zoonotic pathogens in their reservoir hosts is emphasized as a tool to both assess risk factors for spillover and to test hypotheses related to treatment and/or intervention strategies. In conclusion, we highlight the need for smart surveillance, viral discovery efforts and predictive modeling. A shift towards a predictive approach is necessary in today's globalized society because, as the 2009 H1N1 pandemic demonstrated, identification post-emergence is often too late to prevent global spread. Integrating molecular virology and ecological techniques will allow for earlier recognition of potentially dangerous pathogens, ideally before they jump from wildlife reservoirs into human or livestock populations and cause serious public health or conservation issues.

In recent years, the development of computational techniques that identify all the likely targets for a given chemical compound, also termed as the problem of Target Fishing, has been an active area of research. Identification of likely targets of a chemical compound in the early stages of drug discovery helps to understand issues such as selectivity, off-target pharmacology, and toxicity. In this paper, we present a set of techniques whose goal is to rank or prioritize targets in the context of a given chemical compound so that most targets against which this compound may show activity appear higher in the ranked list. These methods are based on our extensions to the SVM and ranking perceptron algorithms for this problem. Our extensive experimental study shows that the methods developed in this work outperform previous approaches 2% to 60% under different evaluation criterions.

Methods that can screen large databases to retrieve a structurally diverse set of compounds with desirable bioactivity properties are critical in the drug discovery and development process. This paper presents a set of such methods that are designed to find compounds that are structurally different to a certain query compound while retaining its bioactivity properties (scaffold hops). These methods utilize various indirect ways of measuring the similarity between the query and a compound that take into account additional information beyond their structure-based similarities. The set of techniques that are presented capture these indirect similarities using approaches based on analyzing the similarity network formed by the query and the database compounds. Experimental evaluation shows that most of these methods substantially outperform previously developed approaches both in terms of their ability to identify structurally diverse active compounds as well as active compounds in general.

Methods that can screen large databases to retrieve a structurally diverse set of compounds with desirable bioactivity properties are critical in the drug discovery and development process. This paper presents a set of such methods, which are designed to find compounds that are structurally different to a certain query compound while retaining its bioactivity properties (scaffold hops). These methods utilize various indirect ways of measuring the similarity between the query and a compound that take into account additional information beyond their structure-based similarities. Two sets of techniques are presented that capture these indirect similarities using approaches based on automatic relevance feedback and on analyzing the similarity network formed by the query and the database compounds. Experimental evaluation shows that many of these methods substantially outperform previously developed approaches both in terms of their ability to identify structurally diverse active compounds as well as active compounds in general.