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Abstract Major depressive disorder (MDD) is a devastating and heterogenous disorder for which there are no approved biomarkers in clinical practice. We recently identified anticipatory hypo-arousal indexed by pupil responses as a candidate mechanism subserving depression symptomatology. Here, we conducted a replication and extension study of these findings. We analyzed a replication sample of 40 unmedicated patients with a diagnosis of depression and 30 healthy control participants, who performed a reward anticipation task while pupil responses were measured. Using a Bayesian modelling approach taking measurement uncertainty into account, we could show that the negative correlation between pupil dilation and symptom load during reward anticipation is replicable within MDD patients, albeit with a lower effect size. Furthermore, with the combined sample of 136 participants (81 unmedicated depressed and 55 healthy control participants), we further showed that reduced pupil dilation in anticipation of reward is inversely associated with anhedonia items of the Beck Depression Inventory in particular. Moreover, using simultaneous fMRI, particularly the right anterior insula as part of the salience network was negatively correlated with depressive symptom load in general and anhedonia items specifically. The present study supports the utility of pupillometry in assessing noradrenergically mediated hypo-arousal during reward anticipation in MDD, a physiological process that appears to subserve anhedonia.

Urine, humidity condensate, and other sources of non-potable water are processed onboard the International Space Station (ISS) by the Water Recovery System (WRS) yielding potable water. While some means of microbial control are in place, including a phosphoric acid/hexavalent chromium urine pretreatment solution, many areas within the WRS are not available for routine microbial monitoring. Due to refurbishment needs, two flex lines from the Urine Processor Assembly (UPA) within the WRS were removed and returned to Earth. The water from within these lines, as well as flush water, was microbially evaluated. Culture and culture-independent analysis revealed the presence of Burkholderia, Paraburkholderia, and Leifsonia. Fungal culture also identified Fusarium and Lecythophora. Hybrid de novo genome analysis of the five distinct Burkholderia isolates identified them as B. contaminans, while the two Paraburkholderia isolates were identified as P. fungorum. Chromate-resistance gene clusters were identified through pangenomic analysis that differentiated these genomes from previously studied isolates recovered from the point-of-use potable water dispenser and/or current NCBI references, indicating that unique populations exist within distinct niches in the WRS. Beyond genomic analysis, fixed samples directly from the lines were imaged by environmental scanning electron microscopy, which detailed networks of fungal-bacterial biofilms. This is the first evidence of biofilm formation within flex lines from the UPA onboard the ISS. For all bacteria isolated, biofilm potential was further characterized, with the B. contaminans isolates demonstrating the most considerable biofilm formation. Moreover, the genomes of the B. contaminans revealed secondary metabolite gene clusters associated with quorum sensing, biofilm formation, antifungal compounds, and hemolysins. The potential production of these gene cluster metabolites was phenotypically evaluated through biofilm, bacterial-fungal interaction, and hemolytic assays. Collectively, these data identify the UPA flex lines as a unique ecological niche and novel area of biofilm growth within the WRS. Further investigation of these organisms and their resistance profiles will enable engineering controls directed toward biofilm prevention in future space station water systems.

Abstract The colony-forming cyanobacteria Trichodesmium spp. are considered one of the most important nitrogen-fixing genera in the warm, low nutrient ocean. Despite this central biogeochemical role, many questions about their evolution, physiology, and trophic interactions remain unanswered. To address these questions, we describe Trichodesmium pangenomic potential via significantly improved genomic assemblies from two isolates and 15 new >50% complete Trichodesmium metagenome-assembled genomes from hand-picked, Trichodesmium colonies spanning the Atlantic Ocean. Phylogenomics identified ~four N2 fixing clades of Trichodesmium across the transect, with T. thiebautii dominating the colony-specific reads. Pangenomic analyses showed that all T. thiebautii MAGs are enriched in COG defense mechanisms and encode a vertically inherited Type III-B Clustered Regularly Interspaced Short Palindromic Repeats and associated protein-based immunity system (CRISPR-Cas). Surprisingly, this CRISPR-Cas system was absent in all T. erythraeum genomes, vertically inherited by T. thiebautii, and correlated with increased signatures of horizontal gene transfer. Additionally, the system was expressed in metaproteomic and transcriptomic datasets and CRISPR spacer sequences with 100% identical hits to field-assembled, putative phage genome fragments were identified. While the currently CO2-limited T. erythraeum is expected to be a ‘winner’ of anthropogenic climate change, their genomic dearth of known phage resistance mechanisms, compared to T. thiebautii, could put this outcome in question. Thus, the clear demarcation of T. thiebautii maintaining CRISPR-Cas systems, while T. erythraeum does not, identifies Trichodesmium as an ecologically important CRISPR-Cas model system, and highlights the need for more research on phage-Trichodesmium interactions.

Abstract To better understand the potential relationship between COVID-19 disease and hologenome microbial community dynamics and functional profiles, we conducted a multivariate taxonomic and functional microbiome comparison of publicly available human bronchoalveolar lavage fluid (BALF) metatranscriptome samples amongst COVID-19 (n = 32), community acquired pneumonia (CAP) (n = 25), and uninfected samples (n = 29). We then performed a stratified analysis based on mortality amongst the COVID-19 cohort with known outcomes of deceased (n = 10) versus survived (n = 15). Our overarching hypothesis was that there are detectable and functionally significant relationships between BALF microbial metatranscriptomes and the severity of COVID-19 disease onset and progression. We observed 34 functionally discriminant gene ontology (GO) terms in COVID-19 disease compared to the CAP and uninfected cohorts, and 21 GO terms functionally discriminant to COVID-19 mortality (q

Drafting is a competitive task in which a set of decision makers choose from a set of resources sequentially, with each resource becoming unavailable once selected. How people make these choices raises basic questions about human decision making, including people's sensitivity to the statistical regularities of the resource environment, their ability to reason about the behavior of their competitors, and their ability to execute and adapt sophisticated strategies in dynamic situations involving uncertainty. Sports provides one real-world example of drafting behavior, in which a set of teams draft players from an available pool in a well-regulated way. Fantasy sport competitions provide potentially large data sets of drafting behavior. We study fantasy football drafting behavior from the 2017 National Football League (NFL) season based on 1350 leagues hosted by the http://sleeper.app platform. We find people are sensitive to some important environmental regularities in the order in which they draft players, but also present evidence that they use a more narrow range of strategies than is likely optimal in terms of team composition. We find little to no evidence for the use of the complicated but well-documented strategy known as handcuffing, and no evidence of irrational influence from individual-level biases for different NFL teams. We do, however, identify a set of circumstances for which there is clear evidence that people's choices are strongly influenced by the immediately preceding choice made by a competitor.

We study whether experts and novices differ in the way they make predictions about National Football League games. In particular, we measure to what extent their predictions are consistent with five environmental regularities that could support decision making based on heuristics. These regularities involve the home team winning more often, the team with the better win-loss record winning more often, the team favored by the majority of media experts winning more often, and two others related to surprise wins and losses in the teams’ previous game. Using signal detection theory and hierarchical Bayesian analysis, we show that expert predictions for the 2017 National Football League (NFL) season generally follow these regularities in a near optimal way, but novice predictions do not. These results support the idea that using heuristics adapted to the decision environment can support accurate predictions and be an indicator of expertise.

There are many ways to measure how people manage risk when they make decisions. A standard approach is to measure risk propensity using self-report questionnaires. An alternative approach is to use decision-making tasks that involve risk and uncertainty, and apply cognitive models of task behavior to infer parameters that measure people's risk propensity. We report the results of a within-participants experiment that used three questionnaires and four decision-making tasks. The questionnaires are the Risk Propensity Scale, the Risk Taking Index, and the Domain Specific Risk Taking Scale. The decision-making tasks are the Balloon Analogue Risk Task, the preferential choice gambling task, the optimal stopping problem, and the bandit problem. We analyze the relationships between the risk measures and cognitive parameters using Bayesian inferences about the patterns of correlation, and using a novel cognitive latent variable modeling approach. The results show that people's risk propensity is generally consistent within different conditions for each of the decision-making tasks. There is, however, little evidence that the way people manage risk generalizes across the tasks, or that it corresponds to the questionnaire measures.

Student evaluations of teaching are widely used to assess instructors and courses. Using a model-based approach and Bayesian methods, we examine how the direction of the scale, labels on scales, and the number of options affect the ratings. We conduct a within-participants experiment in which respondents evaluate instructors and lectures using different scales. We find that people tend to give positive ratings, especially when using letter scales compared with number scales. Furthermore, people tend to use the end-points less often when a scale is presented in reverse. Our model-based analysis allows us to infer how the features of scales shift responses to higher or lower ratings and how they compress scale use to make end-point responses more or less likely. The model also makes predictions about equivalent ratings across scales, which we demonstrate using real-world evaluation data. Our study has implications for the design of scales and for their use in assessment.

Summary: With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility, and reusability of pipeline data; to provide a template for data processing of future spaceflight-relevant datasets; and to encourage cross-analysis of data from other databases with the data available in GeneLab.

Summary: Monitoring microbial communities aboard the International Space Station (ISS) is essential to maintaining astronaut health and the integrity of life-support systems. Using assembled genomes of ISS-derived microbial isolates as references, recruiting metagenomic reads from an astronaut's nasal microbiome revealed no recruitment to a Staphylococcus aureus isolate from samples before launch, yet systematic recruitment across the genome when sampled after 3 months aboard the ISS, with a median percent identity of 100%. This suggests that either a highly similar S. aureus population colonized the astronaut's nasal microbiome while the astronaut was aboard the ISS or that it may have been below detection before spaceflight, instead supporting a shift in community composition. This work highlights the value in generating genomic libraries of microbes from built-environments such as the ISS and demonstrates one way such data can be integrated with metagenomics to facilitate the tracking and monitoring of astronaut microbiomes and health.

The demand for food will outpace productivity of conventional agriculture due to projected growth of the human population, concomitant with shrinkage of arable land, increasing scarcity of freshwater, and a rapidly changing climate. While aquaponics has potential to sustainably supplement food production with minimal environmental impact, there is a need to better characterize the complex interplay between the various components (fish, plant, microbiome) of these systems to optimize scale up and productivity. Here, we investigated how the commonly-implemented practice of continued microbial community transfer from pre-existing systems might promote or impede productivity of aquaponics. Specifically, we monitored plant growth phenotypes, water chemistry, and microbiome composition of rhizospheres, biofilters, and fish feces over 61-days of lettuce (Lactuca sativa var. crispa) growth in nitrogen-limited aquaponic systems inoculated with bacteria that were either commercially sourced or originating from a pre-existing aquaponic system. Lettuce above- and below-ground growth were significantly reduced across replicates treated with a pre-existing aquaponic system inoculum when compared to replicates treated with a commercial inoculum. Reduced productivity was associated with enrichment in specific bacterial genera in plant roots, including Pseudomonas, following inoculum transfer from pre-existing systems. Increased productivity was associated with enrichment of nitrogen-fixing Rahnella in roots of plants treated with the commercial inoculum. Thus, we show that inoculation from a pre-existing system, rather than from a commercial inoculum, is associated with lower yields. Further work will be necessary to test the putative mechanisms involved.

Birnbaum and Quispe-Torreblanca (2018) evaluated a set of six models developed under true-and-error theory against data in which people made choices in repeated gambles. They concluded the three models based on expected utility theory were inadequate accounts of the behavioral data, and argued in favor of the simplest of the remaining three more general models. To reach these conclusions, they used non-Bayesian statistical methods: frequentist point estimation of parameters, bootstrapped confidence intervals of parameters, and null hypothesis significance testing of models. We address the same research goals, based on the same models and the same data, using Bayesian methods. We implement the models as graphical models in JAGS to allow for computational Bayesian analysis. Our results are based on posterior distribution of parameters, posterior predictive checks of descriptive adequacy, and Bayes factors for model comparison. We compare the Bayesian results with those of Birnbaum and Quispe-Torreblanca (2018). We conclude that, while the very general conclusions of the two approaches agree, the Bayesian approach offers better detailed answers, especially for the key question of the evidence the data provide for and against the competing models. Finally, we discuss the conceptual and practical advantages of using Bayesian methods in judgment and decision making research highlighted by this case study.

We consider the recently-developed ``surprisingly popular'' method for aggregating decisions across a group of people (Prelec, Seung and McCoy, 2017). The method has shown impressive performance in a range of decision-making situations, but typically for situations in which the correct answer is already established. We consider the ability of the surprisingly popular method to make predictions in a situation where the correct answer does not exist at the time people are asked to make decisions. Specifically, we tested its ability to predict the winners of the 256 US National Football League (NFL) games in the 2017--2018 season. Each of these predictions used participants who self-rated as ``extremely knowledgeable'' about the NFL, drawn from a set of 100 participants recruited through Amazon Mechanical Turk (AMT). We compare the accuracy and calibration of the surprisingly popular method to a variety of alternatives: the mode and confidence-weighted predictions of the expert AMT participants, the individual and aggregated predictions of media experts, and a statistical Elo method based on the performance histories of the NFL teams. Our results are exploratory, and need replication, but we find that the surprisingly popular method outperforms all of these alternatives, and has reasonable calibration properties relating the confidence of its predictions to the accuracy of those predictions.

We consider the wisdom of the crowd situation in which individuals make binary decisions, and the majority answer is used as the group decision. Using data sets from nine different domains, we examine the relationship between the size of the majority and the accuracy of the crowd decisions. We find empirically that these calibration curves take many different forms for different domains, and the distribution of majority sizes over decisions in a domain also varies widely. We develop a growth model for inferring and interpreting the calibration curve in a domain, and apply it to the same nine data sets using Bayesian methods. The modeling approach is able to infer important qualitative properties of a domain, such as whether it involves decisions that have ground truths or are inherently uncertain. It is also able to make inferences about important quantitative properties of a domain, such as how quickly the crowd accuracy increases as the size of the majority increases. We discuss potential applications of the measurement model, and the need to develop a psychological account of the variety of calibration curves that evidently exist.

The opportunistic pathogens Burkholderia cepacia and Burkholderia contaminans, both genomovars of the Burkholderia cepacia complex (BCC), are frequently cultured from the potable water dispenser (PWD) of the International Space Station (ISS). Here, we sequenced the genomes and conducted phenotypic assays to characterize these Burkholderia isolates. All recovered isolates of the two species fall within monophyletic clades based on phylogenomic trees of conserved single-copy core genes. Within species, the ISS-derived isolates all demonstrate greater than 99% average nucleotide identity (with 95-99% of genomes aligning) and share around 90% of the identified gene clusters from a pangenomic analysis-suggesting that the two groups are each composed of highly similar genomic lineages and their members may have all stemmed from the same two founding populations. The differences that can be observed between the recovered isolates at the pangenomic level are primarily located within putative plasmids. Phenotypically, macrophage intracellularization and lysis occurred at generally similar rates between all ISS-derived isolates, as well as with their respective type-terrestrial strain references. All ISS-derived isolates exhibited antibiotic sensitivity similar to that of the terrestrial reference strains, and minimal differences between isolates were observed. With a few exceptions, biofilm formation rates were generally consistent across each species. And lastly, though isolation date does not necessarily provide any insight into how long a given isolate had been aboard the ISS, none of the assayed physiology correlated with either date of isolation or distances based on nucleotide variation. Overall, we find that while the populations of Burkholderia present in the ISS PWS each maintain virulence, they are likely are not more virulent than those that might be encountered on planet and remain susceptible to clinically used antibiotics.

Phylogenomics is the practice of attempting to infer evolutionary relationships at a genome-level. This is becoming a standard step in the characterization of newly recovered genomes and to direct/constrain further research; yet the process from start to finish of building a de novo phylogenomic tree that is specific to the organisms of interest can still be computationally intractable for many biologists. GToTree is a recently published user-friendly workflow for phylogenomics intended to give more researchers the capability to generate phylogenomic trees to help guide their work. This commentary describes two common applications where GToTree can be helpful and then discusses some things to consider when using the program.

The abundance and diversity of fungi were evaluated in a hypersaline microbial mat from Guerrero Negro, México, using a combination of quantitative polymerase chain reaction (qPCR) amplification of domain-specific primers, and metagenomic sequencing. Seven different layers were analyzed in the mat (Layers 1–7) at single millimeter resolution (from the surface to 7 mm in depth). The number of copies of the 18S rRNA gene of fungi ranged between 106 and 107 copies per g mat, being two logarithmic units lower than of the 16S rRNA gene of bacteria. The abundance of 18S rRNA genes of fungi varied significantly among the layers with layers 2–5 mm from surface contained the highest numbers of copies. Fifty-six fungal taxa were identified by metagenomic sequencing, classified into three different phyla: Ascomycota, Basidiomycota and Microsporidia. The prevalent genera of fungi were Thermothelomyces, Pyricularia, Fusarium, Colletotrichum, Aspergillus, Botrytis, Candida and Neurospora. Genera of fungi identified in the mat were closely related to genera known to have saprotrophic and parasitic lifestyles, as well as genera related to human and plant pathogens and fungi able to perform denitrification. This research suggests that fungi in the mat may participate in nutrient recycling, modification of community composition through parasitic activities, and denitrification.

Cyanobacterial nitrogen fixation supplies bioavailable nitrogen to marine ecosystems, but the mechanisms governing iron and phosphorus co-limitation in elevated CO2remain unknown. Here, the authors show a complex cellular response to co-limitation characterized by changes in growth, cell size, and the proteome.

Depression is a debilitating disorder with high prevalence and socioeconomic cost, but the brain-physiological processes that are altered during depressive states are not well understood. Here, we build on recent findings in macaques that indicate a direct causal relationship between pupil dilation and anterior cingulate cortex mediated arousal during anticipation of reward. We translated these findings to human subjects with concomitant pupillometry/fMRI in a sample of unmedicated participants diagnosed with major depression and healthy controls. We could show that the upregulation and maintenance of arousal in anticipation of reward was disrupted in patients in a symptom-load dependent manner. We could further show that the failure to maintain reward anticipatory arousal showed state-marker properties, as it tracked the load and impact of depressive symptoms independent of prior diagnosis status. Further, group differences of anticipatory arousal and continuous correlations with symptom load were not traceable only at the level of pupillometric responses, but were mirrored also at the neural level within salience network hubs. The upregulation and maintenance of arousal during reward anticipation is a novel translational and well-traceable process that could prove a promising gateway to a physiologically informed patient stratification and targeted interventions.

The key to 650 million years of evolutionary success in jellyfish is adaptability: with alternating benthic and pelagic generations, sexual and asexual reproductive modes, multitudes of body forms and a cosmopolitan distribution, jellyfish are likely to have established a plenitude of microbial associations. Here we explored bacterial assemblages in the scyphozoan jellyfish Chrysaora plocamia (Lesson 1832). Life stages involved in propagation through cyst formation, i.e., the mother polyp, its dormant cysts (podocysts), and polyps recently excysted (excysts) from podocysts – were investigated. Associated bacterial assemblages were assessed using MiSeq Illumina paired-end tag sequencing of the V1V2 region of the 16S rRNA gene. A microbial core-community was identified as present through all investigated life stages, including bacteria with closest relatives known to be key drivers of carbon, nitrogen, phosphorus, and sulfur cycling. Moreover, the fact that half of C. plocamia’s core bacteria were also present in life stages of the jellyfish Aurelia aurita, suggests that this bacterial community might represent an intrinsic characteristic of scyphozoan jellyfish, contributing to their evolutionary success.

Only select prokaryotes can biosynthesize vitamin B12 (i.e., cobalamins), but these organic co-enzymes are required by all microbial life and can be vanishingly scarce across extensive ocean biomes. Although global ocean genome data suggest cyanobacteria to be a major euphotic source of cobalamins, recent studies have highlighted that >95% of cyanobacteria can only produce a cobalamin analog, pseudo-B12, due to the absence of the BluB protein that synthesizes the α ligand 5,6-dimethylbenzimidizole (DMB) required to biosynthesize cobalamins. Pseudo-B12 is substantially less bioavailable to eukaryotic algae, as only certain taxa can intracellularly remodel it to one of the cobalamins. Here we present phylogenetic, metagenomic, transcriptomic, proteomic, and chemical analyses providing multiple lines of evidence that the nitrogen-fixing cyanobacterium Trichodesmium transcribes and translates the biosynthetic, cobalamin-requiring BluB enzyme. Phylogenetic evidence suggests that the Trichodesmium DMB biosynthesis gene, bluB, is of ancient origin, which could have aided in its ecological differentiation from other nitrogen-fixing cyanobacteria. Additionally, orthologue analyses reveal two genes encoding iron-dependent B12 biosynthetic enzymes (cbiX and isiB), suggesting that iron availability may be linked not only to new nitrogen supplies from nitrogen fixation, but also to B12 inputs by Trichodesmium. These analyses suggest that Trichodesmium contains the genus-wide genomic potential for a previously unrecognized role as a source of cobalamins, which may prove to considerably impact marine biogeochemical cycles.

The less-is-more effect predicts that people can be more accurate making paired-comparison decisions when they have less knowledge, in the sense that they do not recognize all of the items in the decision domain. The traditional theoretical explanation is that decisions based on recognizing one alternative but not the other can be more accurate than decisions based on partial knowledge of both alternatives. I present new data that directly test for the less-is-more effect, coming from a task in which participants judge which of two cities is larger and indicate whether they recognize each city. A group-level analysis of these data provides evidence in favor of the less-is-more effect: there is strong evidence people make decisions consistent with recognition, and that these decisions are more accurate than those based on knowledge. An individual-level analysis of the same data, however, provides evidence inconsistent with a simple interpretation of the less-is-more effect: there is no evidence for an inverse-U-shaped relationship between accuracy and recognition, and especially no evidence that individuals who recognize a moderate number of cities outperform individuals who recognize many cities. I suggest a reconciliation of these contrasting findings, based on the systematic change of the accuracy of recognition-based decisions with the underlying recognition rate. In particular, the data show that people who recognize almost none or almost all cities make more accurate decisions by applying the recognition heuristic, when compared to the accuracy achieved by people with intermediate recognition rates. The implications of these findings for precisely defining and understanding the less-is-more effect are discussed, as are the constraints our data potentially place on models of the learning and decision-making processes involved.

Whenever parameter estimates are uncertain or observations are contaminated by measurement error, the Pearson correlation coefficient can severely underestimate the true strength of an association. Various approaches exist for inferring the correlation in the presence of estimation uncertainty and measurement error, but none are routinely applied in psychological research. Here we focus on a Bayesian hierarchical model proposed by Behseta, Berdyyeva, Olson, and Kass (2009) that allows researchers to infer the underlying correlation between error-contaminated observations. We show that this approach may be also applied to obtain the underlying correlation between uncertain parameter estimates as well as the correlation between uncertain parameter estimates and noisy observations. We illustrate the Bayesian modeling of correlations with two empirical data sets; in each data set, we first infer the posterior distribution of the underlying correlation and then compute Bayes factors to quantify the evidence that the data provide for the presence of an association.