The first step is recognizing there is a problem: a methodology for adjusting for variability in disease severity when estimating clinician performance.

BACKGROUND: Adoption of innovations in the field of medicine is frequently hindered by a failure to recognize the condition targeted by the innovation. This is particularly true in cases where recognition requires integration of patient information from different sources, or where disease presentation can be heterogeneous and the recognition step may be easier for some patients than for others. METHODS: We propose a general data-driven metric for clinician recognition that accounts for the variability in patient disease severity and for institutional standards. As a case study, we evaluate the ventilatory management of 362 patients with acute respiratory distress syndrome (ARDS) at a large academic hospital, because clinician recognition of ARDS has been identified as a major barrier to adoption to evidence-based ventilatory management. We calculate our metric for the 48 critical care physicians caring for these patients and examine the relationships between differences in ARDS recognition performance from overall institutional levels and provider characteristics such as demographics, social network position, and self-reported barriers and opinions. RESULTS: Our metric was found to be robust to patient characteristics previously demonstrated to affect ARDS recognition, such as disease severity and patient height. Training background was the only factor in this study that showed an association with physician recognition. Pulmonary and critical care medicine (PCCM) training was associated with higher recognition (ß = 0.63, 95% confidence interval 0.46-0.80, p < 7 × 10- 5). Non-PCCM physicians recognized ARDS cases less frequently and expressed greater satisfaction with the ability to get the information needed for making an ARDS diagnosis (p < 5 × 10- 4), suggesting that lower performing clinicians may be less aware of institutional barriers. CONCLUSIONS: We present a data-driven metric of clinician disease recognition that accounts for variability in patient disease severity and for institutional standards. Using this metric, we identify two unique physician populations with different intervention needs. One population consistently recognizes ARDS and reports barriers vs one does not and reports fewer barriers.

The characteristics of early-stage research into human genes are substantially different from subsequent research.

Throughout the last 2 decades, several scholars observed that present day research into human genes rarely turns toward genes that had not already been extensively investigated in the past. Guided by hypotheses derived from studies of science and innovation, we present here a literature-wide data-driven meta-analysis to identify the specific scientific and organizational contexts that coincided with early-stage research into human genes throughout the past half century. We demonstrate that early-stage research into human genes differs in team size, citation impact, funding mechanisms, and publication outlet, but that generalized insights derived from studies of science and innovation only partially apply to early-stage research into human genes. Further, we demonstrate that, presently, genome biology accounts for most of the initial early-stage research, while subsequent early-stage research can engage other life sciences fields. We therefore anticipate that the specificity of our findings will enable scientists and policymakers to better promote early-stage research into human genes and increase overall innovation within the life sciences.

Aging is associated with a systemic length-associated transcriptome imbalance.

Aging is among the most important risk factors for morbidity and mortality. To contribute toward a molecular understanding of aging, we analyzed age-resolved transcriptomic data from multiple studies. Here, we show that transcript length alone explains most transcriptional changes observed with aging in mice and humans. We present three lines of evidence supporting the biological importance of the uncovered transcriptome imbalance. First, in vertebrates the length association primarily displays a lower relative abundance of long transcripts in aging. Second, eight antiaging interventions of the Interventions Testing Program of the National Institute on Aging can counter this length association. Third, we find that in humans and mice the genes with the longest transcripts enrich for genes reported to extend lifespan, whereas those with the shortest transcripts enrich for genes reported to shorten lifespan. Our study opens fundamental questions on aging and the organization of transcriptomes.

Spreader events and the limitations of projected networks for capturing dynamics on multipartite networks.

Many systems of scientific interest can be conceptualized as multipartite networks. Examples include the spread of sexually transmitted infections, scientific collaborations, human friendships, product recommendation systems, and metabolic networks. In practice, these systems are often studied after projection onto a single class of nodes, losing crucial information. Here, we address a significant knowledge gap by comparing transmission dynamics on temporal multipartite networks and on their time-aggregated unipartite projections to determine the impact of the lost information on our ability to predict the systems' dynamics. We show that the dynamics of transmission models can be dramatically dissimilar on multipartite networks and on their projections at three levels: final outcome, the magnitude of the variability from realization to realization, and overall shape of the temporal trajectory. We find that the ratio of the number of nodes to the number of active edges over the time-aggregation scale determines the ability of projected networks to capture the dynamics on the multipartite network. Finally, we explore which properties of a multipartite network are crucial in generating synthetic networks that better reproduce the dynamical behavior observed in real multipartite networks.

COVID-19 research risks ignoring important host genes due to pre-established research patterns.

It is known that research into human genes is heavily skewed towards genes that have been widely studied for decades, including many genes that were being studied before the productive phase of the Human Genome Project. This means that the genes most frequently investigated by the research community tend to be only marginally more important to human physiology and disease than a random selection of genes. Based on an analysis of 10,395 research publications about SARS-CoV-2 that mention at least one human gene, we report here that the COVID-19 literature up to mid-October 2020 follows a similar pattern. This means that a large number of host genes that have been implicated in SARS-CoV-2 infection by four genome-wide studies remain unstudied. While quantifying the consequences of this neglect is not possible, they could be significant.

A quantitative approach for the analysis of clinician recognition of acute respiratory distress syndrome using electronic health record data.

IMPORTANCE: Despite its efficacy, low tidal volume ventilation (LTVV) remains severely underutilized for patients with acute respiratory distress syndrome (ARDS). Physician under-recognition of ARDS is a significant barrier to LTVV use. We propose a computational method that addresses some of the limitations of the current approaches to automated measurement of whether ARDS is recognized by physicians. OBJECTIVE: To quantify patient and physician factors affecting physicians' tidal volume selection and to build a computational model of physician recognition of ARDS that accounts for these factors. DESIGN, SETTING, AND PARTICIPANTS: In this cross-sectional study, electronic health record data were collected for 361 ARDS patients and 388 non-ARDS hypoxemic (control) patients in nine adult intensive care units at four hospitals between June 24 and December 31, 2013. METHODS: Standardized tidal volumes (mL/kg predicted body weight) were chosen as a proxy for physician decision-making behavior. Using data-science approaches, we quantified the effect of eight factors (six severity of illness, two physician behaviors) on selected standardized tidal volumes in ARDS and control patients. Significant factors were incorporated in computational behavioral models of physician recognition of ARDS. RESULTS: Hypoxemia severity and ARDS documentation in physicians' notes were associated with lower standardized tidal volumes in the ARDS cohort. Greater patient height was associated with lower standardized tidal volumes (which is already normalized for height) in both ARDS and control patients. The recognition model yielded a mean (99% confidence interval) physician recognition of ARDS of 22% (9%-42%) for mild, 34% (19%-49%) for moderate, and 67% (41%-100%) for severe ARDS. CONCLUSIONS AND RELEVANCE: In this study, patient characteristics and physician behaviors were demonstrated to be associated with differences in ventilator management in both ARDS and control patients. Our model of physician ARDS recognition measurement accounts for these clinical variables, providing an electronic approach that moves beyond relying on chart documentation or resource intensive approaches.

Repressive Gene Regulation Synchronizes Development with Cellular Metabolism.

Metabolic conditions affect the developmental tempo of animals. Developmental gene regulatory networks (GRNs) must therefore synchronize their dynamics with a variable timescale. We find that layered repression of genes couples GRN output with variable metabolism. When repressors of transcription or mRNA and protein stability are lost, fewer errors in Drosophila development occur when metabolism is lowered. We demonstrate the universality of this phenomenon by eliminating the entire microRNA family of repressors and find that development to maturity can be largely rescued when metabolism is reduced. Using a mathematical model that replicates GRN dynamics, we find that lowering metabolism suppresses the emergence of developmental errors by curtailing the influence of auxiliary repressors on GRN output. We experimentally show that gene expression dynamics are less affected by loss of repressors when metabolism is reduced. Thus, layered repression provides robustness through error suppression and may provide an evolutionary route to a shorter reproductive cycle.

Large-scale investigation of the reasons why potentially important genes are ignored.

Biomedical research has been previously reported to primarily focus on a minority of all known genes. Here, we demonstrate that these differences in attention can be explained, to a large extent, exclusively from a small set of identifiable chemical, physical, and biological properties of genes. Together with knowledge about homologous genes from model organisms, these features allow us to accurately predict the number of publications on individual human genes, the year of their first report, the levels of funding awarded by the National Institutes of Health (NIH), and the development of drugs against disease-associated genes. By explicitly identifying the reasons for gene-specific bias and performing a meta-analysis of existing computational and experimental knowledge bases, we describe gene-specific strategies for the identification of important but hitherto ignored genes that can open novel directions for future investigation.

A robust data-driven approach identifies four personality types across four large data sets.

Understanding human personality has been a focus for philosophers and scientists for millennia1. It is now widely accepted that there are about five major personality domains that describe the personality profile of an individual2,3. In contrast to personality traits, the existence of personality types remains extremely controversial4. Despite the various purported personality types described in the literature, small sample sizes and the lack of reproducibility across data sets and methods have led to inconclusive results about personality types5,6. Here we develop an alternative approach to the identification of personality types, which we apply to four large data sets comprising more than 1.5 million participants. We find robust evidence for at least four distinct personality types, extending and refining previously suggested typologies. We show that these types appear as a small subset of a much more numerous set of spurious solutions in typical clustering approaches, highlighting principal limitations in the blind application of unsupervised machine learning methods to the analysis of big data.

Social embeddedness in an online weight management programme is linked to greater weight loss.

The obesity epidemic is heightening chronic disease risk globally. Online weight management (OWM) communities could potentially promote weight loss among large numbers of people at low cost. Because little is known about the impact of these online communities, we examined the relationship between individual and social network variables, and weight loss in a large, international OWM programme. We studied the online activity and weight change of 22,419 members of an OWM system during a six-month period, focusing especially on the 2033 members with at least one friend within the community. Using Heckman's sample-selection procedure to account for potential selection bias and data censoring, we found that initial body mass index, adherence to self-monitoring and social networking were significantly correlated with weight loss. Remarkably, greater embeddedness in the network was the variable with the highest statistical significance in our model for weight loss. Average per cent weight loss at six months increased in a graded manner from 4.1% for non-networked members, to 5.2% for those with a few (two to nine) friends, to 6.8% for those connected to the giant component of the network, to 8.3% for those with high social embeddedness. Social networking within an OWM community, and particularly when highly embedded, may offer a potent, scalable way to curb the obesity epidemic and other disorders that could benefit from behavioural changes.

Adoption of a High-Impact Innovation in a Homogeneous Population.

Adoption of innovations, whether new ideas, technologies, or products, is crucially important to knowledge societies. The landmark studies of adoption dealt with innovations having great societal impact (such as antibiotics or hybrid crops) but where determining the utility of the innovation was straightforward (such as fewer side effects or greater yield). Recent large-scale studies of adoption were conducted within heterogeneous populations and focused on products with little societal impact. Here, we focus on a case with great practical significance: adoption by small groups of highly trained individuals of innovations with large societal impact but for which it is impractical to determine the true utility of the innovation. Specifically, we study experimentally the adoption by critical care physicians of a diagnostic assay that complements current protocols for the diagnosis of life-threatening bacterial infections and for which a physician cannot estimate the true accuracy of the assay based on personal experience. We show through computational modeling of the experiment that infection-spreading models-which have been formalized as generalized contagion processes-are not consistent with the experimental data, while a model inspired by opinion models is able to reproduce the empirical data. Our modeling approach enables us to investigate the efficacy of different intervention schemes on the rate and robustness of innovation adoption in the real world. While our study is focused on critical care physicians, our findings have implications for other settings in education, research, and business, where small groups of highly qualified peers make decisions about the adoption of innovations whose utility is difficult if not impossible to gauge.

The possible role of resource requirements and academic career-choice risk on gender differences in publication rate and impact.

Many studies demonstrate that there is still a significant gender bias, especially at higher career levels, in many areas including science, technology, engineering, and mathematics (STEM). We investigated field-dependent, gender-specific effects of the selective pressures individuals experience as they pursue a career in academia within seven STEM disciplines. We built a unique database that comprises 437,787 publications authored by 4,292 faculty members at top United States research universities. Our analyses reveal that gender differences in publication rate and impact are discipline-specific. Our results also support two hypotheses. First, the widely-reported lower publication rates of female faculty are correlated with the amount of research resources typically needed in the discipline considered, and thus may be explained by the lower level of institutional support historically received by females. Second, in disciplines where pursuing an academic position incurs greater career risk, female faculty tend to have a greater fraction of higher impact publications than males. Our findings have significant, field-specific, policy implications for achieving diversity at the faculty level within the STEM disciplines.

The role of mentorship in protégé performance.

The role of mentorship in protégé performance is a matter of importance to academic, business and governmental organizations. Although the benefits of mentorship for protégés, mentors and their organizations are apparent, the extent to which protégés mimic their mentors' career choices and acquire their mentorship skills is unclear. The importance of a science, technology, engineering and mathematics workforce to economic growth and the role of effective mentorship in maintaining a 'healthy' such workforce demand the study of the role of mentorship in academia. Here we investigate one aspect of mentor emulation by studying mentorship fecundity-the number of protégés a mentor trains-using data from the Mathematics Genealogy Project, which tracks the mentorship record of thousands of mathematicians over several centuries. We demonstrate that fecundity among academic mathematicians is correlated with other measures of academic success. We also find that the average fecundity of mentors remains stable over 60 years of recorded mentorship. We further discover three significant correlations in mentorship fecundity. First, mentors with low mentorship fecundities train protégés that go on to have mentorship fecundities 37% higher than expected. Second, in the first third of their careers, mentors with high fecundities train protégés that go on to have fecundities 29% higher than expected. Finally, in the last third of their careers, mentors with high fecundities train protégés that go on to have fecundities 31% lower than expected.

Statistical validation of a global model for the distribution of the ultimate number of citations accrued by papers published in a scientific journal.

A central issue in evaluative bibliometrics is the characterization of the citation distribution of papers in the scientific literature. Here, we perform a large-scale empirical analysis of journals from every field in Thomson Reuters' Web of Science database. We find that only 30 of the 2,184 journals have citation distributions that are inconsistent with a discrete lognormal distribution at the rejection threshold that controls the False Discovery Rate at 0.05. We find that large, multidisciplinary journals are over-represented in this set of 30 journals, leading us to conclude that, within a discipline, citation distributions are lognormal. Our results strongly suggest that the discrete lognormal distribution is a globally accurate model for the distribution of "eventual impact" of scientific papers published in single-discipline journal in a single year that is removed sufficiently from the present date.

Levels of complexity in scale-invariant neural signals.

Many physical and physiological signals exhibit complex scale-invariant features characterized by 1/f scaling and long-range power-law correlations, indicating a possibly common control mechanism. Specifically, it has been suggested that dynamical processes, influenced by inputs and feedback on multiple time scales, may be sufficient to give rise to 1/f scaling and scale invariance. Two examples of physiologic signals that are the output of hierarchical multiscale physiologic systems under neural control are the human heartbeat and human gait. Here we show that while both cardiac interbeat interval and gait interstride interval time series under healthy conditions have comparable 1/f scaling, they still may belong to different complexity classes. Our analysis of the multifractal scaling exponents of the fluctuations in these two signals demonstrates that in contrast to the multifractal behavior found in healthy heartbeat dynamics, gait time series exhibit less complex, close to monofractal behavior. Further, we find strong anticorrelations in the sign and close to random behavior for the magnitude of gait fluctuations at short and intermediate time scales, in contrast to weak anticorrelations in the sign and strong positive correlation for the magnitude of heartbeat interval fluctuations-suggesting that the neural mechanisms of cardiac and gait control exhibit different linear and nonlinear features. These findings are of interest because they underscore the limitations of traditional two-point correlation methods in fully characterizing physiological and physical dynamics. In addition, these results suggest that different mechanisms of control may be responsible for varying levels of complexity observed in physiological systems under neural regulation and in physical systems that possess similar 1/f scaling.

Effectiveness of journal ranking schemes as a tool for locating information.

BACKGROUND: The rise of electronic publishing, preprint archives, blogs, and wikis is raising concerns among publishers, editors, and scientists about the present day relevance of academic journals and traditional peer review. These concerns are especially fuelled by the ability of search engines to automatically identify and sort information. It appears that academic journals can only remain relevant if acceptance of research for publication within a journal allows readers to infer immediate, reliable information on the value of that research. METHODOLOGY/PRINCIPAL FINDINGS: Here, we systematically evaluate the effectiveness of journals, through the work of editors and reviewers, at evaluating unpublished research. We find that the distribution of the number of citations to a paper published in a given journal in a specific year converges to a steady state after a journal-specific transient time, and demonstrate that in the steady state the logarithm of the number of citations has a journal-specific typical value. We then develop a model for the asymptotic number of citations accrued by papers published in a journal that closely matches the data. CONCLUSIONS/SIGNIFICANCE: Our model enables us to quantify both the typical impact and the range of impacts of papers published in a journal. Finally, we propose a journal-ranking scheme that maximizes the efficiency of locating high impact research.

Functional cartography of complex metabolic networks.

High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology that enables us to extract and display information contained in complex networks. Specifically, we demonstrate that we can find functional modules in complex networks, and classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a 'cartographic representation' of complex networks. Metabolic networks are among the most challenging biological networks and, arguably, the ones with most potential for immediate applicability. We use our method to analyse the metabolic networks of twelve organisms from three different superkingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that metabolites that participate in only a few reactions but that connect different modules are more conserved than hubs whose links are mostly within a single module.

Heuristic segmentation of a nonstationary time series.

Many phenomena, both natural and human influenced, give rise to signals whose statistical properties change under time translation, i.e., are nonstationary. For some practical purposes, a nonstationary time series can be seen as a concatenation of stationary segments. However, the exact segmentation of a nonstationary time series is a hard computational problem which cannot be solved exactly by existing methods. For this reason, heuristic methods have been proposed. Using one such method, it has been reported that for several cases of interest-e.g., heart beat data and Internet traffic fluctuations-the distribution of durations of these stationary segments decays with a power-law tail. A potential technical difficulty that has not been thoroughly investigated is that a nonstationary time series with a (scalefree) power-law distribution of stationary segments is harder to segment than other nonstationary time series because of the wider range of possible segment lengths. Here, we investigate the validity of a heuristic segmentation algorithm recently proposed by Bernaola-Galván et al. [Phys. Rev. Lett. 87, 168105 (2001)] by systematically analyzing surrogate time series with different statistical properties. We find that if a given nonstationary time series has stationary periods whose length is distributed as a power law, the algorithm can split the time series into a set of stationary segments with the correct statistical properties. We also find that the estimated power-law exponent of the distribution of stationary-segment lengths is affected by (i) the minimum segment length and (ii) the ratio R identical with sigma(epsilon)/sigma(x), where sigma(x) is the standard deviation of the mean values of the segments and sigma(epsilon) is the standard deviation of the fluctuations within a segment. Furthermore, we determine that the performance of the algorithm is generally not affected by uncorrelated noise spikes or by weak long-range temporal correlations of the fluctuations within segments.

Sexual networks: implications for the transmission of sexually transmitted infections.

The structures of sexual networks are essential for understanding the dynamics of sexually transmitted infections. Standard epidemiological models largely disregard the complex patterns of intimate contacts. Social network analysis offers important insight into how to conceptualize and model social interaction and has the potential to greatly enhance the understanding of disease epidemics.