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Citation metrics are increasingly used to appraise published research. One challenge is whether and how to normalize these metrics to account for differences across scientific fields, age (year of publication), type of document, database coverage, and other factors. We discuss the pros and cons for normalizations using different approaches. Additional challenges emerge when citation metrics need to be combined across multiple papers to appraise the corpus of scientists, institutions, journals, or countries, as well as when trying to attribute credit in multiauthored papers. Different citation metrics may offer complementary insights, but one should carefully consider the assumptions that underlie their calculation. [ABSTRACT FROM AUTHOR]

Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. Despite the noisiness of citation dynamics, as little as 2 years of postpublication data yield accurate predictions about a paper's eventual citation by a clinical article (accuracy = 84%, F1 score = 0.56; compared to 19% accuracy by chance). We found that distinct knowledge flow trajectories are linked to papers that either succeed or fail to influence clinical research. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community's early reaction to a paper. [ABSTRACT FROM AUTHOR]

PubMed is a free search engine for biomedical literature accessed by millions of users from around the world each day. With the rapid growth of biomedical literature—about two articles are added every minute on average—finding and retrieving the most relevant papers for a given query is increasingly challenging. We present Best Match, a new relevance search algorithm for PubMed that leverages the intelligence of our users and cutting-edge machine-learning technology as an alternative to the traditional date sort order. The Best Match algorithm is trained with past user searches with dozens of relevance-ranking signals (factors), the most important being the past usage of an article, publication date, relevance score, and type of article. This new algorithm demonstrates state-of-the-art retrieval performance in benchmarking experiments as well as an improved user experience in real-world testing (over 20% increase in user click-through rate). Since its deployment in June 2017, we have observed a significant increase (60%) in PubMed searches with relevance sort order: it now assists millions of PubMed searches each week. In this work, we hope to increase the awareness and transparency of this new relevance sort option for PubMed users, enabling them to retrieve information more effectively. [ABSTRACT FROM AUTHOR]

Data presentation for scientific publications in small sample size studies has not changed substantially in decades. It relies on static figures and tables that may not provide sufficient information for critical evaluation, particularly of the results from small sample size studies. Interactive graphics have the potential to transform scientific publications from static reports of experiments into interactive datasets. We designed an interactive line graph that demonstrates how dynamic alternatives to static graphics for small sample size studies allow for additional exploration of empirical datasets. This simple, free, web-based tool () demonstrates the overall concept and may promote widespread use of interactive graphics. [ABSTRACT FROM AUTHOR]

Numerous studies demonstrating that statistical errors are common in basic science publications have led to calls to improve statistical training for basic scientists. In this article, we sought to evaluate statistical requirements for PhD training and to identify opportunities for improving biostatistics education in the basic sciences. We provide recommendations for improving statistics training for basic biomedical scientists, including: 1. Encouraging departments to require statistics training, 2. Tailoring coursework to the students’ fields of research, and 3. Developing tools and strategies to promote education and dissemination of statistical knowledge. We also provide a list of statistical considerations that should be addressed in statistics education for basic scientists. [ABSTRACT FROM AUTHOR]

Insects determine their body segments in two different ways. Short-germband insects, such as the flour beetle Tribolium castaneum, use a molecular clock to establish segments sequentially. In contrast, long-germband insects, such as the vinegar fly Drosophila melanogaster, determine all segments simultaneously through a hierarchical cascade of gene regulation. Gap genes constitute the first layer of the Drosophila segmentation gene hierarchy, downstream of maternal gradients such as that of Caudal (Cad). We use data-driven mathematical modelling and phase space analysis to show that shifting gap domains in the posterior half of the Drosophila embryo are an emergent property of a robust damped oscillator mechanism, suggesting that the regulatory dynamics underlying long- and short-germband segmentation are much more similar than previously thought. In Tribolium, Cad has been proposed to modulate the frequency of the segmentation oscillator. Surprisingly, our simulations and experiments show that the shift rate of posterior gap domains is independent of maternal Cad levels in Drosophila. Our results suggest a novel evolutionary scenario for the short- to long-germband transition and help explain why this transition occurred convergently multiple times during the radiation of the holometabolan insects. [ABSTRACT FROM AUTHOR]

The need for replication of initial results has been rediscovered only recently in many fields of research. In preclinical biomedical research, it is common practice to conduct exact replications with the same sample sizes as those used in the initial experiments. Such replication attempts, however, have lower probability of replication than is generally appreciated. Indeed, in the common scenario of an effect just reaching statistical significance, the statistical power of the replication experiment assuming the same effect size is approximately 50%—in essence, a coin toss. Accordingly, we use the provocative analogy of “replicating” a neuroprotective drug animal study with a coin flip to highlight the need for larger sample sizes in replication experiments. Additionally, we provide detailed background for the probability of obtaining a significant p value in a replication experiment and discuss the variability of p values as well as pitfalls of simple binary significance testing in both initial preclinical experiments and replication studies with small sample sizes. We conclude that power analysis for determining the sample size for a replication study is obligatory within the currently dominant hypothesis testing framework. Moreover, publications should include effect size point estimates and corresponding measures of precision, e.g., confidence intervals, to allow readers to assess the magnitude and direction of reported effects and to potentially combine the results of initial and replication study later through Bayesian or meta-analytic approaches. [ABSTRACT FROM AUTHOR]

Our motor commands can be exquisitely timed according to the demands of the environment, and the ability to generate rhythms of different tempos is a hallmark of musical cognition. Yet, the neuronal underpinnings behind rhythmic tapping remain elusive. Here, we found that the activity of hundreds of primate medial premotor cortices (MPCs; pre-supplementary motor area [preSMA] and supplementary motor area [SMA]) neurons show a strong periodic pattern that becomes evident when their responses are projected into a state space using dimensionality reduction analysis. We show that different tapping tempos are encoded by circular trajectories that travelled at a constant speed but with different radii, and that this neuronal code is highly resilient to the number of participating neurons. Crucially, the changes in the amplitude of the oscillatory dynamics in neuronal state space are a signature of duration encoding during rhythmic timing, regardless of whether it is guided by an external metronome or is internally controlled and is not the result of repetitive motor commands. This dynamic state signal predicted the duration of the rhythmically produced intervals on a trial-by-trial basis. Furthermore, the increase in variability of the neural trajectories accounted for the scalar property, a hallmark feature of temporal processing across tasks and species. Finally, we found that the interval-dependent increments in the radius of periodic neural trajectories are the result of a larger number of neurons engaged in the production of longer intervals. Our results support the notion that rhythmic timing during tapping behaviors is encoded in the radial curvature of periodic MPC neural population trajectories. [ABSTRACT FROM AUTHOR]

Reporting of the actual data in graphs and plots increases transparency and enables independent evaluation. On the other hand, data summaries are often used in graphs because they aid interpretation. To democratize state-of-the-art data visualization of raw data with a selection of statistical summaries, a freely available, open-source web app was written using R/shiny that uses the ggplot2 package for generating plots. Users can to choose how to display the data and which of the data summaries to add. In addition, the 95% confidence intervals (95CIs) can be added for visual inferences. By adjusting the visibility of the layers, the visualization of the raw data and their summaries can be tuned for optimal presentation and interpretation. The app is dubbed PlotsOfData and is available at . [ABSTRACT FROM AUTHOR]

It is often claimed that pair bonds preferentially form between individuals that resemble one another. Such assortative mating appears to be widespread throughout the animal kingdom. Yet it is unclear whether the apparent ubiquity of assortative mating arises primarily from mate choice (“like attracts like”), which can be constrained by same-sex competition for mates; from spatial or temporal separation; or from observer, reporting, publication, or search bias. Here, based on a conventional literature search, we find compelling meta-analytical evidence for size-assortative mating in birds (r = 0.178, 95% CI 0.142–0.215, 83 species, 35,591 pairs). However, our analyses reveal that this effect vanishes gradually with increased control of confounding factors. Specifically, the effect size decreased by 42% when we used previously unpublished data from nine long-term field studies, i.e., data free of reporting and publication bias (r = 0.103, 95% CI 0.074–0.132, eight species, 16,611 pairs). Moreover, in those data, assortative mating effectively disappeared when both partners were measured by independent observers or separately in space and time (mean r = 0.018, 95% CI −0.016–0.057). Likewise, we also found no evidence for assortative mating in a direct experimental test for mutual mate choice in captive populations of Zebra finches (r = −0.020, 95% CI −0.148–0.107, 1,414 pairs). These results highlight the importance of unpublished data in generating unbiased meta-analytical conclusions and suggest that the apparent ubiquity of assortative mating reported in the literature is overestimated and may not be driven by mate choice or mating competition for preferred mates. [ABSTRACT FROM AUTHOR]

Increasingly complex statistical models are being used for the analysis of biological data. Recent commentary has focused on the ability to compute the same outcome for a given dataset (reproducibility). We argue that a reproducible statistical analysis is not necessarily valid because of unique patterns of nonindependence in every biological dataset. We advocate that analyses should be evaluated with known-truth simulations that capture biological reality, a process we call “analysis validation.” We review the process of validation and suggest criteria that a validation project should meet. We find that different fields of science have historically failed to meet all criteria, and we suggest ways to implement meaningful validation in training and practice. [ABSTRACT FROM AUTHOR]

Biological processes in living cells are often carried out by gene networks in which signals and reactions are integrated through network hubs. Despite their functional importance, it remains unclear to what extent network hubs are evolvable and how alterations impact long-term evolution. We investigated these issues using heat shock protein 90 (Hsp90), a central hub of proteostasis networks. When native Hsp90 in Saccharomyces cerevisiae cells was replaced by the ortholog from hypersaline-tolerant Yarrowia lipolytica that diverged from S. cerevisiae about 270 million years ago, the cells exhibited improved growth in hypersaline environments but compromised growth in others, indicating functional divergence in Hsp90 between the two yeasts. Laboratory evolution shows that evolved Y. lipolytica-HSP90–carrying S. cerevisiae cells exhibit a wider range of phenotypic variation than cells carrying native Hsp90. Identified beneficial mutations are involved in multiple pathways and are often pleiotropic. Our results show that cells adapt to a heterologous Hsp90 by modifying different subnetworks, facilitating the evolution of phenotypic diversity inaccessible to wild-type cells. [ABSTRACT FROM AUTHOR]

An important hallmark of science is the transparency and reproducibility of scientific results. Over the last few years, internet-based technologies have emerged that allow for a representation of the scientific process that goes far beyond traditional methods and analysis descriptions. Using these often freely available tools requires a suite of skills that is not necessarily part of a curriculum in the life sciences. However, funders, journals, and policy makers increasingly require researchers to ensure complete reproducibility of their methods and analyses. To close this gap, we designed an introductory course that guides students towards a reproducible science workflow. Here, we outline the course content and possible extensions, report encountered challenges, and discuss how to integrate such a course in existing curricula. [ABSTRACT FROM AUTHOR]

CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler’s infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows. [ABSTRACT FROM AUTHOR]

Decision-making is usually accompanied by metacognition, through which a decision maker monitors uncertainty regarding a decision and may then consequently revise the decision. These decisional metacognitive processes can occur prior to or in the absence of feedback. However, the neural mechanisms of metacognition remain controversial. One theory proposes an independent neural system for metacognition in the prefrontal cortex (PFC); the other, that metacognitive processes coincide and overlap with the systems used for the decision-making process per se. In this study, we devised a novel “decision–redecision” paradigm to investigate the neural metacognitive processes involved in redecision as compared to the initial decision-making process. The participants underwent a perceptual decision-making task and a rule-based decision-making task during functional magnetic resonance imaging (fMRI). We found that the anterior PFC, including the dorsal anterior cingulate cortex (dACC) and lateral frontopolar cortex (lFPC), were more extensively activated after the initial decision. The dACC activity in redecision positively scaled with decisional uncertainty and correlated with individual metacognitive uncertainty monitoring abilities—commonly occurring in both tasks—indicating that the dACC was specifically involved in decision uncertainty monitoring. In contrast, the lFPC activity seen in redecision processing was scaled with decision uncertainty reduction and correlated with individual accuracy changes—positively in the rule-based decision-making task and negatively in the perceptual decision-making task. Our results show that the lFPC was specifically involved in metacognitive control of decision adjustment and was subject to different control demands of the tasks. Therefore, our findings support that a separate neural system in the PFC is essentially involved in metacognition and further, that functions of the PFC in metacognition are dissociable. [ABSTRACT FROM AUTHOR]

Understanding the rat neurochemical connectome is fundamental for exploring neuronal information processing. By using advanced data mining, supervised machine learning, and network analysis, this study integrates over 5 decades of neuroanatomical investigations into a multiscale, multilayer neurochemical connectome of the rat brain. This neurochemical connectivity database (ChemNetDB) is supported by comprehensive systematically-determined receptor distribution maps. The rat connectome has an onion-type structural organization and shares a number of structural features with mesoscale connectomes of mouse and macaque. Furthermore, we demonstrate that extremal values of graph theoretical measures (e.g., degree and betweenness) are associated with evolutionary-conserved deep brain structures such as amygdala, bed nucleus of the stria terminalis, dorsal raphe, and lateral hypothalamus, which regulate primitive, yet fundamental functions, such as circadian rhythms, reward, aggression, anxiety, and fear. The ChemNetDB is a freely available resource for systems analysis of motor, sensory, emotional, and cognitive information processing. [ABSTRACT FROM AUTHOR]

An animal’s ability to survive depends on its sensory systems being able to adapt to a wide range of environmental conditions, by maximizing the information extracted and reducing the noise transmitted. The visual system does this by adapting to luminance and contrast. While luminance adaptation can begin at the retinal photoreceptors, contrast adaptation has been shown to start at later stages in the retina. Photoreceptors adapt to changes in luminance over multiple time scales ranging from tens of milliseconds to minutes, with the adaptive changes arising from processes within the phototransduction cascade. Here we show a new form of adaptation in cones that is independent of the phototransduction process. Rather, it is mediated by voltage-gated ion channels in the cone membrane and acts by changing the frequency response of cones such that their responses speed up as the membrane potential modulation depth increases and slow down as the membrane potential modulation depth decreases. This mechanism is effectively activated by high-contrast stimuli dominated by low frequencies such as natural stimuli. However, the more generally used Gaussian white noise stimuli were not effective since they did not modulate the cone membrane potential to the same extent. This new adaptive process had a time constant of less than a second. A critical component of the underlying mechanism is the hyperpolarization-activated current, Ih, as pharmacologically blocking it prevented the long- and mid- wavelength sensitive cone photoreceptors (L- and M-cones) from adapting. Consistent with this, short- wavelength sensitive cone photoreceptors (S-cones) did not show the adaptive response, and we found they also lacked a prominent Ih. The adaptive filtering mechanism identified here improves the information flow by removing higher-frequency noise during lower signal-to-noise ratio conditions, as occurs when contrast levels are low. Although this new adaptive mechanism can be driven by contrast, it is not a contrast adaptation mechanism in its strictest sense, as will be argued in the Discussion. [ABSTRACT FROM AUTHOR]

Here, I argue that computational thinking and techniques are so central to the quest of understanding life that today all biology is computational biology. Computational biology brings order into our understanding of life, it makes biological concepts rigorous and testable, and it provides a reference map that holds together individual insights. The next modern synthesis in biology will be driven by mathematical, statistical, and computational methods being absorbed into mainstream biological training, turning biology into a quantitative science. [ABSTRACT FROM AUTHOR]

There is a need for formalised diagrams that both summarise current biological pathway knowledge and support modelling approaches that explain and predict their behaviour. Here, we present a new, freely available modelling framework that includes a biologist-friendly pathway modelling language (mEPN), a simple but sophisticated method to support model parameterisation using available biological information; a stochastic flow algorithm that simulates the dynamics of pathway activity; and a 3-D visualisation engine that aids understanding of the complexities of a system’s dynamics. We present example pathway models that illustrate of the power of approach to depict a diverse range of systems. [ABSTRACT FROM AUTHOR]

The human brain can be parcellated into diverse anatomical areas. We investigated whether rhythmic brain activity in these areas is characteristic and can be used for automatic classification. To this end, resting-state MEG data of 22 healthy adults was analysed. Power spectra of 1-s long data segments for atlas-defined brain areas were clustered into spectral profiles (“fingerprints”), using k-means and Gaussian mixture (GM) modelling. We demonstrate that individual areas can be identified from these spectral profiles with high accuracy. Our results suggest that each brain area engages in different spectral modes that are characteristic for individual areas. Clustering of brain areas according to similarity of spectral profiles reveals well-known brain networks. Furthermore, we demonstrate task-specific modulations of auditory spectral profiles during auditory processing. These findings have important implications for the classification of regional spectral activity and allow for novel approaches in neuroimaging and neurostimulation in health and disease. [ABSTRACT FROM AUTHOR]

One of the most fundamental features of the human brain is its ability to detect and attend to salient goal-relevant events in a flexible manner. The salience network (SN), anchored in the anterior insula and the dorsal anterior cingulate cortex, plays a crucial role in this process through rapid detection of goal-relevant events and facilitation of access to appropriate cognitive resources. Here, we leverage the subsecond resolution of large multisession fMRI datasets from the Human Connectome Project and apply novel graph-theoretical techniques to investigate the dynamic spatiotemporal organization of the SN. We show that the large-scale brain dynamics of the SN are characterized by several distinctive and robust properties. First, the SN demonstrated the highest levels of flexibility in time-varying connectivity with other brain networks, including the frontoparietal network (FPN), the cingulate–opercular network (CON), and the ventral and dorsal attention networks (VAN and DAN). Second, dynamic functional interactions of the SN were among the most spatially varied in the brain. Third, SN nodes maintained a consistently high level of network centrality over time, indicating that this network is a hub for facilitating flexible cross-network interactions. Fourth, time-varying connectivity profiles of the SN were distinct from all other prefrontal control systems. Fifth, temporal flexibility of the SN uniquely predicted individual differences in cognitive flexibility. Importantly, each of these results was also observed in a second retest dataset, demonstrating the robustness of our findings. Our study provides fundamental new insights into the distinct dynamic functional architecture of the SN and demonstrates how this network is uniquely positioned to facilitate interactions with multiple functional systems and thereby support a wide range of cognitive processes in the human brain. [ABSTRACT FROM AUTHOR]