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Abstract The ability Gram-negative pathogens have at adapting and protecting themselves against antibiotics has increasingly become a public health threat. Data-driven models identifying molecular properties that correlate with outer membrane (OM) permeation and growth inhibition while avoiding efflux could guide the discovery of novel classes of antibiotics. Here we evaluate 174 molecular descriptors in 1260 antimicrobial compounds and study their correlations with antibacterial activity in Gram-negative Pseudomonas aeruginosa. The descriptors are derived from traditional approaches quantifying the compounds’ intrinsic physicochemical properties, together with, bacterium-specific from ensemble docking of compounds targeting specific MexB binding pockets, and all-atom molecular dynamics simulations in different subregions of the OM model. Using these descriptors and the measured inhibitory concentrations, we design a statistical protocol to identify predictors of OM permeation/inhibition. We find consistent rules across most of our data highlighting the role of the interaction between the compounds and the OM. An implementation of the rules uncovered in our study is shown, and it demonstrates the accuracy of our approach in a set of previously unseen compounds. Our analysis sheds new light on the key properties drug candidates need to effectively permeate/inhibit P. aeruginosa, and opens the gate to similar data-driven studies in other Gram-negative pathogens.

Surprise is a key component of many learning experiences, and yet its precise computational role, and how it changes with age, remain debated. One major challenge is that surprise often occurs jointly with other variables, such as uncertainty and outcome probability. To assess how humans learn from surprising events, and whether aging affects this process, we studied choices while participants learned from bandits with either Gaussian or bi-modal outcome distributions, which decoupled outcome probability, uncertainty, and surprise. A total of 102 participants (51 older, aged 50-73; 51 younger, 19-30 years) chose between three bandits, one of which had a bimodal outcome distribution. Behavioral analyses showed that both age-groups learned the average of the bimodal bandit less well. A trial-by-trial analysis indicated that participants performed choice reversals immediately following large absolute prediction errors, consistent with heightened sensitivity to surprise. This effect was stronger in older adults. Computational models indicated that learning rates in younger as well as older adults were influenced by surprise, rather than uncertainty, but also suggested large interindividual variability in the process underlying learning in our task. Our work bridges between behavioral economics research that has focused on how outcomes with low probability affect choice in older adults, and reinforcement learning work that has investigated age differences in the effects of uncertainty and suggests that older adults overly adapt to surprising events, even when accounting for probability and uncertainty effects.

Abstract Background Expiratory time constant (τ) objectively assesses the speed of exhalation and can guide adjustments of the respiratory rate and the I:E ratio with the goal of achieving complete exhalation. Multiple methods of obtaining τ are available, but they have not been compared. The purpose of this study was to compare six different methods to obtain τ and to test if the exponentially decaying flow corresponds to the measured time constants. Methods In this prospective study, pressure, flow, and volume waveforms of 30 postoperative patients undergoing volume (VCV) and pressure-controlled ventilation (PCV) were obtained using a data acquisition device and analyzed. τ was measured as the first 63% of the exhaled tidal volume (VT) and compared to the calculated τ as the product of expiratory resistance (RE) and respiratory system compliance (CRS), or τ derived from passive flow/volume waveforms using previously published equations as proposed by Aerts, Brunner, Guttmann, and Lourens. We tested if the duration of exponentially decaying flow during exhalation corresponded to the duration of the predicted second and third τ, based on multiples of the first measured τ. Results Mean (95% CI) measured τ was 0.59 (0.57–0.62) s and 0.60 (0.58–0.63) s for PCV and VCV (p = 0.45), respectively. Aerts method showed the shortest values of all methods for both modes: 0.57 (0.54–0.59) s for PCV and 0.58 (0.55–0.61) s for VCV. Calculated (CRS * RE) and Brunner’s τ were identical with mean τ of 0.64 (0.61–0.67) s for PCV and 0.66 (0.63–069) s for VCV. Mean Guttmann’s τ was 0.64 (0.61–0.68) in PCV and 0.65 (0.62–0.69) in VCV. Comparison of each τ method between PCV and VCV was not significant. Predicted time to exhale 95% of the VT (i.e., 3*τ) was 1.77 (1.70–1.84) s for PCV and 1.80 (1.73–1.88) s for VCV, which was significantly longer than measured values: 1.27 (1.22–1.32) for PCV and 1.30 (1.25–1.35) s for VCV (p

Abstract How valuable a choice option is often changes over time, making the prediction of value changes an important challenge for decision making. Prior studies identified a cognitive map in the hippocampal-entorhinal system that encodes relationships between states and enables prediction of future states, but does not inherently convey value during prospective decision making. In this fMRI study, participants predicted changing values of choice options in a sequence, forming a trajectory through an abstract two-dimensional value space. During this task, the entorhinal cortex exhibited a grid-like representation with an orientation aligned to the axis through the value space most informative for choices. A network of brain regions, including ventromedial prefrontal cortex, tracked the prospective value difference between options. These findings suggest that the entorhinal grid system supports the prediction of future values by representing a cognitive map, which might be used to generate lower-dimensional value signals to guide prospective decision making.

Abstract We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained to predict a binary outcome constructed from reported suicide, suicide attempt, and overdose diagnoses with varying choices of study design and prediction methodology. Each model used twenty cross-sectional and 190 longitudinal variables observed in eight time intervals covering 7.5 years prior to the time of prediction. Ensembles of seven base models were created and fine-tuned with ten variables expected to change with study design and outcome definition in order to predict suicide and combined outcome in a prospective cohort. The ensemble models achieved c-statistics of 0.73 on 2-year suicide risk and 0.83 on the combined outcome when predicting on a prospective cohort of $$\sim$$ ∼ 4.2 M veterans. The ensembles rely on nonlinear base models trained using a matched retrospective nested case-control (Rcc) study cohort and show good calibration across a diversity of subgroups, including risk strata, age, sex, race, and level of healthcare utilization. In addition, a linear Rcc base model provided a rich set of biological predictors, including indicators of suicide, substance use disorder, mental health diagnoses and treatments, hypoxia and vascular damage, and demographics.

Abstract Updating beliefs in changing environments can be driven by gradually adapting expectations or by relying on inferred hidden states (i.e. contexts), and changes therein. Previous work suggests that increased reliance on context could underly fear relapse phenomena that hinder clinical treatment of anxiety disorders. We test whether trait anxiety variations in a healthy population influence how much individuals rely on hidden-state inference. In a Pavlovian learning task, participants observed cues that predicted an upcoming electrical shock with repeatedly changing probability, and were asked to provide expectancy ratings on every trial. We show that trait anxiety is associated with steeper expectation switches after contingency reversals and reduced oddball learning. Furthermore, trait anxiety is related to better fit of a state inference, compared to a gradual learning, model when contingency changes are large. Our findings support previous work suggesting hidden-state inference as a mechanism behind anxiety-related to fear relapse phenomena.

Introduction: Spontaneous intracranial hypotension (SIH) is an important cause of devastating headaches and caused by CSF-leaks in the spine. Research question: The aim of this analysis was to gain an overview of the progress of research on SIH over time. The global publication landscape relating to SIH was analyzed and comparisons between regions were made. Material and methods: A bibliometric analysis was performed by searching for research articles on SIH in PubMed published between 1983 and 2022. Countries responsible for the publications were ranked by the sum of citations. An average annual growth rate was calculated and the density of SIH publications per 100 000 physicians was determined. Results: We identified 974 articles. In 1983 only one SIH patient was reported; in 2021 the number of patients had increased to 4230. The average annual growth rate of SIH publications during this period was 12.7%. The most common publication type were case reports (n = 570). The most common medical specialty of the first author was neurology (n = 251) followed by neurosurgery (n = 250) and radiology (n = 191). Although most publications originated from the United States of America (USA), South Korea had the highest density of SIH investigators (37.86 publications per 100 000 medical doctors). The most cited paper (296 citations) was published in 2006 in JAMA (USA). Discussion and conclusion: Research on SIH has increased exponentially over the past four decades. The international community of SIH researchers is growing, and with it the opportunities for global networks involved in research, treatment, and patient education.

Abstract The ventromedial prefrontal-cortex (vmPFC) is known to contain expected value signals that inform our choices. But expected values even for the same stimulus can differ by task. In this study, we asked how the brain flexibly switches between such value representations in a task-dependent manner. Thirty-five participants alternated between tasks in which either stimulus color or motion predicted rewards. We show that multivariate vmPFC signals contain a rich representation that includes the current task state or context (motion/color), the associated expected value, and crucially, the irrelevant value of the alternative context. We also find that irrelevant value representations in vmPFC compete with relevant value signals, interact with task-state representations and relate to behavioral signs of value competition. Our results shed light on vmPFC’s role in decision making, bridging between its role in mapping observations onto the task states of a mental map, and computing expected values for multiple states.

Decisions that require taking effort costs into account are ubiquitous in real life. The neural common currency theory hypothesizes that a particular neural network integrates different costs (e.g., risk) and rewards into a common scale to facilitate value comparison. Although there has been a surge of interest in the computational and neural basis of effort-related value integration, it is still under debate if effort-based decision-making relies on a domain-general valuation network as implicated in the neural common currency theory. Therefore, we comprehensively compared effort-based and risky decision-making using a combination of computational modeling, univariate and multivariate fMRI analyses, and data from two independent studies. We found that effort-based decision-making can be best described by a power discounting model that accounts for both the discounting rate and effort sensitivity. At the neural level, multivariate decoding analyses indicated that the neural patterns of the dorsomedial prefrontal cortex (dmPFC) represented subjective value across different decision-making tasks including either effort or risk costs, although univariate signals were more diverse. These findings suggest that multivariate dmPFC patterns play a critical role in computing subjective value in a task-independent manner and thus extend the scope of the neural common currency theory.

Abstract Discovery of reliable signatures for the empirical diagnosis of neurological diseases—both infectious and non-infectious—remains unrealized. One of the primary challenges encountered in such studies is the lack of a comprehensive database representative of a signature background that exists in healthy individuals, and against which an aberrant event can be assessed. For neurological insults and injuries, it is important to understand the normal profile in the neuronal (cerebrospinal fluid) and systemic fluids (e.g., blood). Here, we present the first comparative multi-omic human database of signatures derived from a population of 30 individuals (15 males, 15 females, 23–74 years) of serum and cerebrospinal fluid. In addition to empirical signatures, we also assigned common pathways between serum and CSF. Together, our findings provide a cohort against which aberrant signature profiles in individuals with neurological injuries/disease can be assessed—providing a pathway for comprehensive diagnostics and therapeutics discovery.

Aging is associated with changes in spatial navigation behavior. In addition to an overall performance decline, older adults tend to rely more on proximal location cue information than on environmental boundary information during spatial navigation compared to young adults. The fact that older adults are more susceptible to errors during spatial navigation might be partly attributed to deficient dopaminergic modulation of hippocampal and striatal functioning. Hence, elevating dopamine levels might differentially modulate spatial navigation and memory performance in young and older adults. In this work, we administered levodopa (L-DOPA) in a double-blind within-subject, placebo-controlled design and recorded functional neuroimaging while young and older adults performed a 3D spatial navigation task in which boundary geometry or the position of a location cue were systematically manipulated. An age by intervention interaction on the neural level revealed an upregulation of brain responses in older adults and a downregulation of responses in young adults within the medial temporal lobe (including hippocampus and parahippocampus) and brainstem, during memory retrieval. Behaviorally, L-DOPA had no effect on older adults’ overall memory performance; however, older adults whose spatial memory improved under L-DOPA also showed a shift towards more boundary processing under L-DOPA. In young adults, L-DOPA induced a decline in spatial memory performance in task-naïve participants. These results are consistent with the inverted-U-shaped hypothesis of dopamine signaling and cognitive function and suggest that increasing dopamine availability improves hippocampus-dependent place learning in some older adults.

A seminal report by Peter H. Pearse (1988; Rising to the Challenge: A New Policy for Canada’s Freshwater Fisheries, Canadian Wildlife Federation, Ottawa) outlined 62 policy recommendations focused on the management of Canada’s inland fisheries. Over three decades later, freshwater ecosystems and inland fisheries in Canada are still facing similar challenges with many emerging ones that could not have been foreseen. Here, we reflect on the contemporary relevance of the Pearse Report and propose recommendations that policy makers should consider. Broadly, our recommendations are: (1) manage fishes, fisheries, and habitat using a holistic co-management framework, with clearly defined fishery jurisdictions and partnerships with Indigenous governments; (2) engage in transparent, inclusive, and agile research to support decision-making; (3) facilitate knowledge co-production, involving interdisciplinary projects with diverse groups of actors and sectors including Indigenous Peoples, anglers, policy makers, scientists/researchers, governments, and the public; (4) embrace technological advances to support freshwater fisheries stock assessment and management; and (5) align policy and management activities in Canada with global initiatives related to increasing the sustainability of inland fisheries. We advocate for an updated comprehensive report such as the Pearse Report to ensure that we embrace robust, inclusive, and sustainable management strategies and policies for Canada’s inland fisheries for the next 30 years. It is time to again rise to the challenge.

Background and purpose: The choice between invasive and non-invasive treatment of diaphyseal forearm fractures in children can be difficult. We investigated the trends in choice of treatment of pediatric diaphyseal forearm fractures over a 20-year period. Patients and methods: This is a population-based register study with data from 1997 to 2016 retrieved from the Danish National Patient Registry. The primary outcome was choice of primary treatment within 1 week divided into non-invasive treatment (casting only or closed reduction including casting) and invasive (Kirshner wires, intramedullary nailing [IMN], and open reduction internal fixation [ORIF]). The secondary outcomes were further sub-analyses on invasive treatment and age groups. Results: 36,244 diaphyseal forearm fractures were investigated, yielding a mean incidence of 172 per 105/year. The proportion of fractures treated invasively increased from 1997 to 2016, from 4% to 23%. The use of Kirschner wires increased from 1% to 9%, IMN increased from 1% to 14%, and ORIF decreased from 2% to 1%. The changes were evident in all age groups but smaller in the 0–3-year age group. Conclusion: We found an increase in invasive treatment of pediatric diaphyseal forearm fractures over the investigated period. A change in invasive methods was also found, as the rate of IMN increased over the investigated period and became the predominant surgical treatment choice.

Previous studies indicate a role of dopamine in spatial navigation. Although neural representations of direction are an important aspect of spatial cognition, it is not well understood whether dopamine directly affects these representations, or only impacts other aspects of spatial brain function. Moreover, both dopamine and spatial cognition decline sharply during age, raising the question which effect dopamine has on directional signals in the brain of older adults. To investigate these questions, we used a double-blind cross-over L-DOPA/Placebo intervention design in which 43 younger and 37 older adults navigated in a virtual spatial environment while undergoing functional magnetic resonance imaging (fMRI). We studied the effect of L-DOPA, a dopamine precursor, on fMRI activation patterns that encode spatial walking directions that have previously been shown to lose specificity with age. This was done in predefined regions of interest, including the early visual cortex, retrosplenial cortex, and hippocampus. Classification of brain activation patterns associated with different walking directions was improved across all regions following L-DOPA administration, suggesting that dopamine broadly enhances neural representations of direction. No evidence for differences between regions was found. In the hippocampus these results were found in both age groups, while in the retrosplenial cortex they were only observed in younger adults. Taken together, our study provides evidence for a link between dopamine and the specificity of neural responses during spatial navigation. Significance Statement: The sense of direction is an important aspect of spatial navigation, and neural representations of direction can be found throughout a large network of space-related brain regions. But what influences how well these representations track someone’s true direction? Using a double-blind cross-over L-DOPA/Placebo intervention design, we find causal evidence that the neurotransmitter dopamine impacts the fidelity of direction selective neural representations in the human hippocampus and retrosplenial cortex. Interestingly, the effect of L-DOPA was either equally present or even smaller in older adults, despite the well-known age related decline of dopamine. These results provide novel insights into how dopamine shapes the neural representations that underlie spatial navigation.

Abstract Spatial learning can be based on intramaze cues and environmental boundaries. These processes are predominantly subserved by striatal- and hippocampal-dependent circuitries, respectively. Maturation and aging processes in these brain regions may affect lifespan differences in their contributions to spatial learning. We independently manipulated an intramaze cue or the environment’s boundary in a navigation task in 27 younger children (6–8 years), 30 older children (10–13 years), 29 adolescents (15–17 years), 29 younger adults (20–35 years) and 26 older adults (65–80 years) to investigate lifespan age differences in the relative prioritization of either information. Whereas learning based on an intramaze cue showed earlier maturation during the progression from younger to later childhood and remained relatively stable across adulthood, maturation of boundary-based learning was more protracted towards peri-adolescence and showed strong aging-related decline. Furthermore, individual differences in prioritizing intramaze cue- over computationally more demanding boundary-based learning was positively associated with cognitive processing fluctuations and this association was partially mediated by spatial working memory capacity during adult, but not during child development. This evidence reveals different age gradients of two modes of spatial learning across the lifespan, which seem further influenced by individual differences in cognitive processing fluctuations and working memory, particularly during aging.

Abstract The optimal time when surgery can be safely performed after stroke is unknown. The purpose of this study was to investigate how cardiac surgery timing after stroke impacts postoperative outcomes between 2011–2017 were reviewed. Variables were extracted from the institutional Society of Thoracic Surgeons database, statewide patient registry, and medical records. Subjects were classified based upon presence of endocarditis and further grouped by timing of preoperative stroke relative to cardiac surgery: Recent (stroke within two weeks before surgery), Intermediate (between two and six weeks before), and Remote (greater than six weeks before). Postoperative outcomes were compared amongst groups. 157 patients were included: 54 in endocarditis and 103 in non-endocarditis, with 47 in Recent, 26 in Intermediate, and 84 in Remote. 30-day mortality and postoperative stroke rate were similar across the three subgroups for both endocarditis and non-endocarditis. Of patients with postoperative stroke, mortality was 30% (95% CI 4.6–66). Timing of cardiac surgery after stroke occurrence does not seem to affect postoperative stroke or mortality. If postoperative stroke does occur, subsequent stroke-related mortality is high.

Non-invasive measurement of fast neural activity with spatial precision in humans is difficult. Here, the authors show how fMRI can be used to detect sub-second neural sequences in a localized fashion and report fast replay of images in visual cortex that occurred independently of the hippocampus.

Goal: We describe the relationship between mean arterial pressure (MAP) and glomerular filtration rate (GFR) since therapies affecting MAP can have large effects on kidney function. Methods: We developed a closed-loop, steady-state mechanistic model of the human kidney with a reduced parameter set estimated from measurements. Results: The model was first validated against literature models. Further, GFR was validated against intensive care patient data (root mean squared error (RMSE) 13.5 mL/min) and against hypertensive patients receiving sodium nitroprusside (SNP) (RMSE less than 5 mL/min). A sensitivity analysis of the model reinforced the fact that vascular resistance is inversely related to GFR and showed that changes to either vascular resistance or renal autoregulation cause a significant change in sodium concentration in the descending limb of Henle. Conclusions: This model can be used to determine the impact of MAP on GFR and overall kidney health. The modeling framework lends itself to personalization of the model to a specific human.

Goal: Alveolar compliance is a main determinant of lung airflow. The compliance of the alveoli is a function of their tissue fiber elasticity, fiber volume, and surface tension. The compliance varies during respiration because of the nonlinear nature of fiber elasticity and the time-varying surface tension coating the alveoli. Respiratory conditions, like acute respiratory distress syndrome (ARDS) and idiopathic pulmonary fibrosis (IPF) affect fiber elasticity, fiber volume and surface tension. In this paper, we study the alveolar tissue fibers and surface tension effects on lung mechanics. Methods: To better understand the lungs, we developed a physiology-based mathematical model to 1) describe the effect of tissue fiber elasticity, fiber volume and surface tension on alveolar compliance, and 2) the effect of time-varying alveolar compliance on lung mechanics for healthy, ARDS and IPF conditions. Results: We first present the model sensitivity analysis to show the effects of model parameters on the lung mechanics variables. Then, we perform model simulation and validate on healthy non-ventilated subjects and ventilated ARDS or IPF patients. Finally, we assess the robustness and stability of this dynamic system. Conclusions: We developed a mathematical model of the lung mechanics comprising alveolar tissue and surfactant properties that generates reasonable lung pressures and volumes compared to healthy, ARDS, and IPF patient data.

Heart-lung interaction mechanisms are generally not well understood. Mechanical ventilation, for example, accentuates such interactions and could compromise cardiac activity. Thereby, assessment of ventilation-induced changes in cardiac function is considered an unmet clinical need. We believe that mathematical models of the human cardiopulmonary system can provide invaluable insights into such cardiorespiratory interactions. In this article, we aim to use a mathematical model to explain heart-lung interaction phenomena and provide physiologic hypotheses to certain contradictory experimental observations during mechanical ventilation. To accomplish this task, we highlight three model components that play a crucial role in heart-lung interactions: 1) pericardial membrane, 2) interventricular septum, and 3) pulmonary circulation that enables pulmonary capillary compression due to lung inflation. Evaluation of the model’s response under simulated ventilation scenarios shows good agreement with experimental data from the literature. A sensitivity analysis is also presented to evaluate the relative impact of the model’s highlighted components on the cyclic ventilation-induced changes in cardiac function.

Understanding the performance of collaborative governance regimes (CGRs) necessitates an understanding of how stakeholders and their interactions evolve over time. However, few studies assess the evolution of the structure or process dynamics of CGRs over time. This paper contributes to our understanding of the longitudinal dynamics of CGRs. We apply a modified grounded theory approach to a dataset of collaboration case studies to develop empirically-based theory about how often CGRs persist over time, how different components of CGRs evolve over time, what conditions support or hinder this evolution, and how different developmental trajectories lead to differences in the outputs and outcomes achieved by these groups. We find that CGRs follow a variety of trajectories, from failing to initiate, to achieving their work in a relatively quick time, to sustaining their operations for decades, to incurring slow or rapid declines in health. Additionally, many characteristics of CGRs, including leadership, collaborative process, accountability, and outputs/outcomes, peak at the midpoint of the observed time, suggesting that at some point, even stable and healthy collaborations incur some decline in their robustness. As an exploratory study, this work highlights the need for a better accounting of how CGRs develop, sustain, evolve, and decline over time.

Abstract Background Common bacterial blight (CBB) caused by Xanthomonas phaseoli pv. phaseoli and Xanthomonas citri pv. fuscans is one of the major threats to common bean crops (Phaseolus vulgaris L.). Resistance to CBB is particularly complex as 26 quantitative resistance loci to CBB have been described so far. To date, transcriptomic studies after CBB infection have been very scarce and the molecular mechanisms underlying susceptibility or resistance are largely unknown. Results We sequenced and annotated the genomes of two common bean genotypes being either resistant (BAT93) or susceptible (JaloEEP558) to CBB. Reciprocal BLASTp analysis led to a list of 20,787 homologs between these genotypes and the common bean reference genome (G19833), which provides a solid dataset for further comparative analyses. RNA-Seq after inoculation with X. phaseoli pv. phaseoli showed that the susceptible genotype initiated a more intense and diverse biological response than the resistant genotype. Resistance was linked to upregulation of the salicylic acid pathway and downregulation of photosynthesis and sugar metabolism, while susceptibility was linked to downregulation of resistance genes and upregulation of the ethylene pathway and of genes involved in cell wall modification. Conclusions This study helps better understanding the mechanisms occurring during the early colonization phase of common bean by Xanthomonas and unveils new actors potentially important for resistance and susceptibility to CBB. We discuss the potential link between the pathways induced during bean colonization and genes induced by transcription activator-like effectors (TALEs), as illustrated in other Xanthomonas pathovars.

Background. There is limited “real-world” evidence examining treatment modalities and outcomes in patients with symptomatic peripheral arterial disease undergoing endovascular treatment of femoropopliteal (FP) in-stent restenosis (ISR). Materials and Methods. We compared outcomes in 2,895 patients from the XLPAD registry (NCT01904851) between 2006 and 2019 treated for FP ISR (n = 347) and non-ISR (n = 2,548) lesions. Primary endpoint included major adverse limb events (MALE) at 1 year, a composite of all-cause death, target limb repeat revascularization, or major amputation. Results. ISR patients were more frequently on antiplatelet (94.5% vs 89.4%, p=0.007) and statin (68.9% vs 60.3%, p=0.003) therapies. Lesion length was similar (ISR: 145 ± 99 mm vs. non-ISR: 142 ± 99 mm, p=0.55). Fewer treated ISR lesions were chronic total occlusions (47.3% vs. 53.7%, p=0.02) and severely calcified (22.4% vs. 44.7%, p

Children often perform worse than adults on tasks that require focused attention. While this is commonly regarded as a sign of incomplete cognitive development, a broader attentional focus could also endow children with the ability to find novel solutions to a given task. To test this idea, we investigated children’s ability to discover and use novel aspects of the environment that allowed them to improve their decision-making strategy. Participants were given a simple choice task in which the possibility of strategy improvement was neither mentioned by instructions nor encouraged by explicit error feedback. Among 47 children (8—10 years of age) who were instructed to perform the choice task across two experiments, 27.5% showed a full strategy change. This closely matched the proportion of adults who had the same insight (28.2% of n = 39). The amount of erroneous choices, working memory capacity and inhibitory control, in contrast, indicated substantial disadvantages of children in task execution and cognitive control. A task difficulty manipulation did not affect the results. The stark contrast between age-differences in different aspects of cognitive performance might offer a unique opportunity for educators in fostering learning in children.

Fitting Susceptible-Infected-Recovered (SIR) models to incidence data is problematic when not all infected individuals are reported. Assuming an underlying SIR model with general but known distribution for the time to recovery, this paper derives the implied differential-integral equations for observed incidence data when a fixed fraction of newly infected individuals are not observed. The parameters of the resulting system of differential equations are identifiable. Using these differential equations, we develop a stochastic model for the conditional distribution of current disease incidence given the entire past history of reported cases. We estimate the model parameters using Bayesian Markov Chain Monte-Carlo sampling of the posterior distribution. We use our model to estimate the transmission rate and fraction of asymptomatic individuals for the current Coronavirus 2019 outbreak in eight American Countries: the United States of America, Brazil, Mexico, Argentina, Chile, Colombia, Peru, and Panama, from January 2020 to May 2021. Our analysis reveals that the fraction of reported cases varies across all countries. For example, the reported incidence fraction for the United States of America varies from 0.3 to 0.6, while for Brazil it varies from 0.2 to 0.4.

Summary: Foraging is a common decision problem in natural environments. When new exploitable sites are always available, a simple optimal strategy is to leave a current site when its return falls below a single average reward rate. Here, we examined foraging in a more structured environment, with a limited number of sites that replenished at different rates and had to be revisited. When participants could choose sites, they visited fast-replenishing sites more often, left sites at higher levels of reward, and achieved a higher net reward rate. Decisions to exploit-or-leave a site were best explained with a computational model that included both the average reward rate for the environment and reward information about the unattended sites. This suggests that unattended sites influence leave decisions, in foraging environments where sites can be revisited.

Abstract Freshwater biodiversity loss is one of the greatest environmental threats in our changing world. Although declines have been reported extensively in the literature, much less attention has been devoted to solving the freshwater biodiversity crisis relative to other ecosystems. The recently proposed Emergency Recovery Plan for Freshwater Biodiversity (Tickner et al., 2020, BioScience, 70(4), 330–342) outlines an ambitious but necessary set of overarching actions that can help “bend the curve” for freshwater biodiversity declines. This plan is timely given the present opportunity to adjust freshwater biodiversity targets in international biodiversity agreements and to encourage meeting targets of relevant Sustainable Development Goals. Yet, relying solely on a trickle down from such agreements to national and local scales will likely take too long, given the immediate urgency of the situation. Here, we advocate for a broader, concerted effort from all actors to ensure the Emergency Recovery Plan meaningfully influences the actions of practitioners at a local scale. We outline the roles and responsibilities of actors involved with policy, research, professional bodies and societies, advocacy, and industry, as well as practitioners themselves, in achieving this goal. It is our hope that this overview facilitates the real‐world actions needed to execute the Emergency Recovery Plan so that we can indeed “bend the curve” for freshwater biodiversity.

This case reports on iatrogenic intramural hematoma of the left internal mammary artery (LIMA) identified on intravascular ultrasound. This case illustrates the importance of intravascular imaging to identify the presence and extent of an intraluminal hematoma that is not visualized on angiography and that was the cause of ischemic symptoms following LIMA injection. (Level of Difficulty: Advanced.)

Background: Severe malarial anaemia (SMA) is a leading cause of childhood mortality in holoendemic Plasmodium falciparum regions. Methods: To gain an improved understanding of SMA pathogenesis, whole genome and transcriptome profiling was performed in Kenyan children (n = 144, 3–36 months) with discrete non-SMA and SMA phenotypes. Leukocyte associated immunoglobulin like receptor 1 (LAIR1) emerged as a predictor of susceptibility to SMA (P A); rs2287827 (18835G>A)] and clinical outcomes were investigated in individuals (n = 1512,

Abstract Background Habitat is the foundation for healthy and productive fisheries. For fish that require substrate for spawning, lack of appropriate spawning substrate is inherently limiting and a lack of access to suitable spawning habitat will lead to population collapse. To ensure management resources are being allocated wisely and conservation targets are being achieved, there is an increased need to consider the effectiveness of techniques to enhance or create habitat that has been lost. The aim of this systematic review was to assess the effectiveness of techniques currently used to create or enhance spawning habitat for substrate-spawning (including vegetation-spawning) fish in temperate regions, and to investigate the factors that influence the effectiveness of habitat creation or enhancement. Methods Searches for primary research studies on the effect of spawning habitat creation or enhancement for substrate-spawning fish were conducted in bibliographic databases, on websites and an online search engine, through evidence call-outs, social media, and Advisory Team contacts, and in the bibliographies of relevant reviews. All articles were screened at two stages (title and abstract, and full-text), with consistency checks being performed at each stage. Relevant articles were critically appraised and meta-data and quantitative data were extracted into a database. All included studies were described narratively and studies that met the criteria for meta-analysis were analyzed quantitatively. Review findings A total of 75 studies from 64 articles were included in this systematic review and underwent data extraction and critical appraisal. The majority of these studies were from North America (78.1%) and a large percentage (63.7%) targeted salmonids. We conducted a meta-analysis using data from 22 studies with 53 data sets. Available evidence suggests that the addition or alteration of rock material (e.g., gravel, cobble) was effective in increasing the abundance of substrate-spawning fish compared to controls, with a taxonomic bias towards salmonids (5/6 data sets). The addition of plant material (e.g., large woody debris) with or without physical alterations to the waterbody (e.g., excavation) was also effective in increasing substrate-spawning fish abundance on average compared to controls. Egg life stages (i.e., nests, redds, zygotes or developing embryos) were associated with larger increases in abundance with habitat creation or enhancement than age-0 life stages (i.e., alevin, fry, young-of-the-year). We found no detectable effect of ecosystem type (lotic vs. lentic waterbodies) or time since habitat creation or enhancement on intervention effectiveness for fish abundance. Conclusions The synthesis of available evidence suggests that the addition or alteration of rock material (e.g., addition of gravel, substrate washing) was an effective means of enhancing spawning habitat, but results may only be applicable for salmonids. Furthermore, the synthesis suggests that on average, the addition of plant material with or without waterbody modifications was also effective at increasing fish abundance. Overall, we were limited in our ability to address many of the questions that stakeholders have regarding the circumstances under which spawning habitat creation or enhancement is effective for substrate-spawning fish. Before we can provide recommendations with a higher level of certainty, we need to improve research and reporting, and expand research focus to include a broader range of species and intervention types. We provide several recommendations aimed at researchers and practitioners to improve the quality of evidence being generated.

Human noroviruses (HuNoVs) are highly contagious and cause acute and sporadic diarrheal illness in all age groups. In addition, chronic infections occur in immunocompromised cancer and transplant patients.

Efflux pump avoidance and inhibition are desired properties for the optimization of antibacterial activities against Gram-negative bacteria. However, molecular and physicochemical interactions defining the interface between compounds and efflux pumps remain poorly understood. We identified properties that correlate with efflux avoidance and inhibition, are predictive of similar features in structurally diverse compounds, and allow researchers to distinguish between efflux substrates, inhibitors, and avoiders in P. aeruginosa

Summary: Dense surface glycosylation on the HIV-1 envelope (Env) protein acts as a shield from the adaptive immune system. However, the molecular complexity and flexibility of glycans make experimental studies a challenge. Here we have integrated high-throughput atomistic modeling of fully glycosylated HIV-1 Env with graph theory to capture immunologically important features of the shield topology. This is the first complete all-atom model of HIV-1 Env SOSIP glycan shield that includes both oligomannose and complex glycans, providing physiologically relevant insights of the glycan shield. This integrated approach including quantitative comparison with cryo-electron microscopy data provides hitherto unexplored details of the native shield architecture and its difference from the high-mannose glycoform. We have also derived a measure to quantify the shielding effect over the antigenic protein surface that defines regions of relative vulnerability and resilience of the shield and can be harnessed for rational immunogen design.

[This corrects the article DOI: 10.1371/journal.pcbi.1008149.].

Learning and generalization in spatial domains is often thought to rely on a "cognitive map", representing relationships between spatial locations. Recent research suggests that this same neural machinery is also recruited for reasoning about more abstract, conceptual forms of knowledge. Yet, to what extent do spatial and conceptual reasoning share common computational principles, and what are the implications for behavior? Using a within-subject design we studied how participants used spatial or conceptual distances to generalize and search for correlated rewards in successive multi-armed bandit tasks. Participant behavior indicated sensitivity to both spatial and conceptual distance, and was best captured using a Bayesian model of generalization that formalized distance-dependent generalization and uncertainty-guided exploration as a Gaussian Process regression with a radial basis function kernel. The same Gaussian Process model best captured human search decisions and judgments in both domains, and could simulate realistic learning curves, where we found equivalent levels of generalization in spatial and conceptual tasks. At the same time, we also find characteristic differences between domains. Relative to the spatial domain, participants showed reduced levels of uncertainty-directed exploration and increased levels of random exploration in the conceptual domain. Participants also displayed a one-directional transfer effect, where experience in the spatial task boosted performance in the conceptual task, but not vice versa. While confidence judgments indicated that participants were sensitive to the uncertainty of their knowledge in both tasks, they did not or could not leverage their estimates of uncertainty to guide exploration in the conceptual task. These results support the notion that value-guided learning and generalization recruit cognitive-map dependent computational mechanisms in spatial and conceptual domains. Yet both behavioral and model-based analyses suggest domain specific differences in how these representations map onto actions.

With practice, humans improve their performance in a task by either optimizing a known strategy or discovering a novel, potentially more fruitful strategy. We investigated the neural processes underlying these two fundamental abilities by applying fMRI in a task with two possible alternative strategies. For analysis we combined time-resolved network analysis with Coherence Density Peak Clustering (Allegra et al., 2017), univariate GLM, and multivariate pattern classification. Converging evidence showed that the posterior portion of the default network, i.e. the precuneus and the angular gyrus bilaterally, has a central role in the optimization of the current strategy. These regions encoded the relevant spatial information, increased the strength of local connectivity as well as the long-distance connectivity with other relevant regions in the brain (e.g., visual cortex, dorsal attention network). The connectivity increase was proportional to performance optimization. By contrast, the anterior portion of the default network (i.e. medial prefrontal cortex) and the rostral portion of the fronto-parietal network were associated with new strategy discovery: an early increase of local and long-range connectivity centered on these regions was only observed in the subjects who would later shift to a new strategy. Overall, our findings shed light on the dynamic interactions between regions related to attention and with cognitive control, underlying the balance between strategy exploration and exploitation. Results suggest that the default network, far from being “shut-down” during task performance, has a pivotal role in the background exploration and monitoring of potential alternative courses of action.

Background Thermogenic fitness drink formulas (TFD) have been shown to increase energy expenditure and markers of lipid metabolism. The purpose of the current study was to compare TFD formulas containing different caffeine concentrations versus a placebo drink on energy expenditure and lipid metabolism at rest and during exercise. Methods Thirty-two recreationally active participants (22.9 ± 0.7 y, 167.1 ± 1.4 cm, 68.8 ± 2.0 kg, 24.0 ± 1.2% fat) who were regular caffeine consumers, participated in this randomized, double-blind, crossover design study. Participants reported to the laboratory on three occasions, each of which required consumption of either a TFD containing 140 mg or 100 mg of caffeine or a placebo. Baseline measurements of resting energy expenditure (REE) and resting fat oxidation (RFO) were assessed using indirect calorimetry as well as measurements of serum glycerol concentration. Measurements were repeated at 30, 60, 90 min post-ingestion. Following resting measures, participants completed a graded exercise test to determine maximal oxygen uptake (V̇O2max), maximal fat oxidation (MFO) and the exercise intensity that elicits MFO (Fatmax), and total energy expenditure (EE). Results A significant interaction was shown for REE (p

[This corrects the article DOI: 10.1371/journal.pone.0225858.].

Abstract Background Host specialization is a hallmark of numerous plant pathogens including bacteria, fungi, oomycetes and viruses. Yet, the molecular and evolutionary bases of host specificity are poorly understood. In some cases, pathological convergence is observed for individuals belonging to distant phylogenetic clades. This is the case for Xanthomonas strains responsible for common bacterial blight of bean, spread across four genetic lineages. All the strains from these four lineages converged for pathogenicity on common bean, implying possible gene convergences and/or sharing of a common arsenal of genes conferring the ability to infect common bean. Results To search for genes involved in common bean specificity, we used a combination of whole-genome analyses without a priori, including a genome scan based on k-mer search. Analysis of 72 genomes from a collection of Xanthomonas pathovars unveiled 115 genes bearing DNA sequences specific to strains responsible for common bacterial blight, including 20 genes located on a plasmid. Of these 115 genes, 88 were involved in successive events of horizontal gene transfers among the four genetic lineages, and 44 contained nonsynonymous polymorphisms unique to the causal agents of common bacterial blight. Conclusions Our study revealed that host specificity of common bacterial blight agents is associated with a combination of horizontal transfers of genes, and highlights the role of plasmids in these horizontal transfers.

Background/Objective: Wintertime thermal inversions in narrow mountain valleys create a ceiling effect, increasing concentration of small particulate matter (PM2.5). Despite potential health risks, many people continue to exercise outdoors in thermal inversions. This study measured the effects of ambient PM2.5 exposure associated with a typical thermal inversion on exercise performance, pulmonary function, and biological markers of inflammation. Methods: Healthy, active adults (5 males, 11 females) performed two cycle ergometer time trials outdoors in a counterbalanced design: 1) low ambient PM2.5 concentrations ( .05) for PM2.5 concentration and the measured variables. Conclusion: An acute bout of vigorous exercise during an AQI of “yellow” did not diminish exercise performance in healthy adults, nor did it have a negative effect on pulmonary function or biological health markers. These variables might not be sensitive to small changes from acute, mild PM2.5 exposure. Keywords: Air pollution, Cycle ergometry, Pulmonary function, Time trial, Vigorous exercise

An amendment to this paper has been published and can be accessed via the original article.

The detailed study of prehospital injury death is critical to advancing trauma and emergency care, as circumstance and causality have significant implications for the development of mitigation strategies. Though there is no true ‘Golden Hour,’ the time from injury to care is a critical element in the analysis matrix, particularly in patients with severe injury. Currently, there is no standard method for the assessment of time to definitive care after injury among prehospital deaths. This article describes a methodology to estimate total prehospital time and distance for trauma patients transported via ground emergency medical services and helicopter emergency medical services using a geographic information system. Data generated using this method, along with medical examiner and field investigation reports, will be used to estimate the potential survivability of prehospital trauma deaths occurring in five US states and the District of Columbia as part of the Multi-Institutional Multidisciplinary Injury Mortality Investigation in the Civilian Pre-Hospital Environment study. One goal of this work is to develop standard metrics for the assessment of total prehospital time and distance, which can be used in the future for more complex spatial analyses to gain a deeper understanding of trauma center access. Results will be used to identify high priority areas for research and development in injury prevention, trauma system performance improvement, and public health.

The activity of neural populations in the brains of humans and animals can exhibit vastly different spatial patterns when faced with different tasks or environmental stimuli. The degrees of similarity between these neural activity patterns in response to different events are used to characterize the representational structure of cognitive states in a neural population. The dominant methods of investigating this similarity structure first estimate neural activity patterns from noisy neural imaging data using linear regression, and then examine the similarity between the estimated patterns. Here, we show that this approach introduces spurious bias structure in the resulting similarity matrix, in particular when applied to fMRI data. This problem is especially severe when the signal-to-noise ratio is low and in cases where experimental conditions cannot be fully randomized in a task. We propose Bayesian Representational Similarity Analysis (BRSA), an alternative method for computing representational similarity, in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data. By marginalizing over the unknown activity patterns, we can directly estimate this covariance structure from imaging data. This method offers significant reductions in bias and allows estimation of neural representational similarity with previously unattained levels of precision at low signal-to-noise ratio, without losing the possibility of deriving an interpretable distance measure from the estimated similarity. The method is closely related to Pattern Component Model (PCM), but instead of modeling the estimated neural patterns as in PCM, BRSA models the imaging data directly and is suited for analyzing data in which the order of task conditions is not fully counterbalanced. The probabilistic framework allows for jointly analyzing data from a group of participants. The method can also simultaneously estimate a signal-to-noise ratio map that shows where the learned representational structure is supported more strongly. Both this map and the learned covariance matrix can be used as a structured prior for maximum a posteriori estimation of neural activity patterns, which can be further used for fMRI decoding. Our method therefore paves the way towards a more unified and principled analysis of neural representations underlying fMRI signals. We make our tool freely available in Brain Imaging Analysis Kit (BrainIAK).

As they approach a traffic light, drivers and pedestrians monitor the color (instructed stimulus feature) and/or the position of the signal (covarying stimulus feature) for response selection. Many studies have pointed out that instructions can effectively determine the stimulus features used for response selection in a task. This leaves open whether and how practice with a correlating alternative stimulus feature can lead to a strategy change from an instructed to a learned variant of performing the task. To address this question, we instructed participants to respond to the position of a stimulus within a reference frame, at the same time, during task performance, an unmentioned second stimulus feature, the color, covaried with stimulus position and allowed the use of an alternative response strategy. To assess the impact of the non-instructed stimulus feature of color on response selection throughout practice, the spatial position of the stimulus was ambiguous on some trials. Group average increases in color usage were based on a mixture of (1) participants who, despite extended practice on the covariation, exclusively relied on the instructed stimulus feature and (2) those who abruptly started to rely heavily on stimulus color to select responses in ambiguous trials. When the instructed and uninstructed feature predicted different actions, choices were still biased by the uninstructed color feature, albeit more weakly. A second experiment showed that the influence of color generalized across frequently and infrequently presented combinations of position and color. Strategy changes were accompanied by awareness in both experiments. The results suggest that incidental covariation learning can trigger spontaneous voluntary strategy change involving a re-configuration of the instructed task set.

Around the world, scavenging birds such as vultures and condors have been experiencing drastic population declines. Scavenging birds have a distinct digestive process to deal with higher amounts of bacteria in their primary diet of carcasses in varying levels of decay. These observations motivate us to present an analysis of captive and healthy California condor (Gymnogyps californianus) microbiomes to characterize a population raised together under similar conditions. Shotgun metagenomic DNA sequences were analyzed from fecal and cloacal samples of captive birds. Classification of shotgun DNA sequence data with peptide signatures using the Sequedex package provided both phylogenetic and functional profiles, as well as individually annotated reads for targeted confirmatory analysis. We observed bacterial species previously associated with birds and gut microbiomes, including both virulent and opportunistic pathogens such as Clostridium perfringens, Propionibacterium acnes, Shigella flexneri, and Fusobacterium mortiferum, common flora such as Lactobacillus johnsonii, Lactobacillus ruminus, and Bacteroides vulgatus, and mucosal microbes such as Delftia acidovorans, Stenotrophomonas maltophilia, and Corynebacterium falsnii. Classification using shotgun metagenomic reads from phylogenetic marker genes was consistent with, and more specific than, analysis based on 16S rDNA data. Classification of samples based on either phylogenetic or functional profiles of genomic fragments differentiated three types of samples: fecal, mature cloacal and immature cloacal, with immature birds having approximately 40% higher diversity of microbes.

Seasonal flu is an acute respiratory disease that exacts a massive toll on human populations, healthcare systems and economies. The disease is caused by an enveloped Influenza virus containing eight ribonucleoprotein (RNP) complexes. Each RNP incorporates multiple copies of nucleoprotein (NP), a fragment of the viral genome (vRNA), and a viral RNA-dependent RNA polymerase (POL), and is responsible for packaging the viral genome and performing critical functions including replication and transcription. A complete model of an Influenza RNP in atomic detail can elucidate the structural basis for viral genome functions, and identify potential targets for viral therapeutics. In this work we construct a model of a complete Influenza A RNP complex in atomic detail using multiple sources of structural and sequence information and a series of homology-modeling techniques, including a motif-matching fragment assembly method. Our final model provides a rationale for experimentally-observed changes to viral polymerase activity in numerous mutational assays. Further, our model reveals specific interactions between the three primary structural components of the RNP, including potential targets for blocking POL-binding to the NP-vRNA complex. The methods developed in this work open the possibility of elucidating other functionally-relevant atomic-scale interactions in additional RNP structures and other biomolecular complexes.

We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained to predict a binary outcome constructed from reported suicide, suicide attempt, and overdose diagnoses with varying choices of study design and prediction methodology. Each model used twenty cross-sectional and 190 longitudinal variables observed in eight time intervals covering 7.5 years prior to the time of prediction. Ensembles of seven base models were created and fine-tuned with ten variables expected to change with study design and outcome definition in order to predict suicide and combined outcome in a prospective cohort. The ensemble models achieved c-statistics of 0.73 on 2-year suicide risk and 0.83 on the combined outcome when predicting on a prospective cohort of [Formula: see text] 4.2 M veterans. The ensembles rely on nonlinear base models trained using a matched retrospective nested case-control (Rcc) study cohort and show good calibration across a diversity of subgroups, including risk strata, age, sex, race, and level of healthcare utilization. In addition, a linear Rcc base model provided a rich set of biological predictors, including indicators of suicide, substance use disorder, mental health diagnoses and treatments, hypoxia and vascular damage, and demographics.