This study postulates that underlying environmental conditions and a susceptible population’s socio-economic status should be explored simultaneously to adequately understand a vector borne disease infection risk. Here we focus on West Nile Virus (WNV), a mosquito borne pathogen, as a case study for spatial data visualization of environmental characteristics of a vector’s habitat alongside human demographic composition for understanding potential public health risks of infectious disease. Multiple efforts have attempted to predict WNV environmental risk, while others have documented factors related to human vulnerability to the disease. However, analytical modeling that combines the two is difficult due to the number of potential explanatory variables, varying spatial resolutions of available data, and differing research questions that drove the initial data collection. We propose that the use of geovisualization may provide a glimpse into the large number of potential variables influencing the disease and help distill them into a smaller number that might reveal hidden and unknown patterns. This geovisual look at the data might then guide development of analytical models that can combine environmental and socio-economic data.Geovisualization was used to integrate an environmental model of the disease vector’s habitat alongside human risk factors derived from socio-economic variables. County level WNV incidence rates from California, USA, were used to define a geographically constrained study area where environmental and socio-economic data were extracted from 1,133 census tracts. A previously developed mosquito habitat model that was significantly related to WNV infected dead birds was used to describe the environmental components of the study area. Self-organizing maps found 49 clusters, each of which contained census tracts that were more similar to each other in terms of WNV environmental and socio-economic data. Parallel coordinate plots permitted visualization of each cluster’s data, uncovering patterns that allowed final census tract mapping exposing complex spatial patterns contained within the clusters.Our results suggest that simultaneously visualizing environmental and socio-economic data supports a fuller understanding of the underlying spatial processes for risks to vector-borne disease. Unexpected patterns were revealed in our study that would be useful for developing future multilevel analytical models. For example, when the cluster that contained census tracts with the highest median age was examined, it was determined that those census tracts only contained moderate mosquito habitat risk. Likewise, the cluster that contained census tracts with the highest mosquito habitat risk had populations with moderate median age. Finally, the cluster that contained census tracts with the highest WNV human incidence rates had unexpectedly low mosquito habitat risk. Variations in infectious disease risk occur across environmental gradients and population groups. Such variations often manifest themselves in geographic space and can be attributed to complex interactions between the environment, population, and behavior . The undWest Nile Virus, a vector-borne disease that is primarily spread by the Culex species of mosquitos, was first detected in the United States in 1999 . SeveralIn our earlier study  geostatiThis hypothesis suggests that an integrated approach to understanding the relationships of environmental variables and human population demographics on WNV risk should improve our ability to explore large numbers of possible combinations of the processes in order to discover potential hidden but useful patterns. However, Disease vectors and pathogen reservoirs typically intersect within the context of specific environmental factors , while t® software by Esri , or (2) Topologically Integrated Geographic Encoding and Referencing system (TIGER) by the U.S. Census Bureau, which is in the Public Domain.In the United States, California ranks third in total area , and hasX and Y dimensions were the geographic centroids of each county, and WNV incidence rates for the county provided the Z dimension. Those data were then analyzed to generate a spatial 1-standard deviation ellipse (SDE), representing the contiguous region that contained 1-standard deviation of the reported human WNV incidence rates in California. SDE mapping is a common method used to identify spatial direction trends of attribute data associated with geographical features. It has been widely used for geographically identifying disease and crime trends  that represented the results of our earlier GWR model  was deriNumerous studies have shown that a susceptible population’s risk can be influenced by demographic and socio-economic conditions. For example, zillioninfo.com). SomVis was/is an integrated software tool consisting of three interactively linked visualizations that can help focus attention on patterns of similarity in complex data sets. The three visualizations used were: (1) a SOM  and socio-economic (human demographic) processes occurring in each census tract within a 1-SDE. The approach consisted of utilizing the risk map with multivariate visualization techniques to facilitate the exploration and understanding of complex environmental and socio-economic patterns within the California data. The analysis was facilitated with SomVis, originally an open source Java application, that has now been ported to a web-based service (1) a SOM  to perfo1) a SOM  to visua1) a SOM . These tOur study highlights the potential of combining these tools along with GIS to detect and analyze different hidden patterns within the complex multivariate data. The coupling of these techniques provides an interesting platform for analyzing larger datasets by integrating it into a spatially-explicit disease model or by using it for near-real time disease monitoring. This user interactive data exploration platform helps identify clusters of complex high dimensional datasets while preserving the topological relationships between data vectors.I. SOM is used to reduce the dimensionality of data for data visualization purposes while retaining the most information contained within the database. It is a unique partitioning clustering method, which segments multivariate data into non-overlapping clusters and projects them on a two-dimensional layout. n dimensional space onto a two-dimensional layout by using n equidistant parallel vertical axes, where n is the number of variables in the data set. Each vertical axis represents one variable and is linearly scaled using its minimum and maximum values. Each cluster is displayed as a horizontal polyline intersecting each of the vertical axes at the point that corresponds to the respective attribute value for this data element. The thickness of the polyline is proportional to the number of elements in the node (number of census tracts). The PCP can help visualize the data either using combinations of variables (cluster level) or for each individual variable (data item level).II. A PCP maps III. Geographic mapping of which census tracts fall within any specific cluster or clusters provides a visual perspective of where the socio-economic and environmental variables of most interest are located. SomVis refers to these as a Geomap and they represent the spatial distribution of multivariate patterns. The Geomap provides a spatial perspective to clusters of similar variables identified using PCPs. These three visual components allow an array of user-controlled interactions that link spatial patterns to the underlying data.r2 of 0.71 . Those results agreed with other research  variables were extracted for all census tracts within the ellipse. California has 8,040 census tracts, with 1,133 intersecting the 1-SDE. The census tracts within the ellipse averaged 8,780 hectares in size. Environmental and socio-economic data were considered simultaneously with SOM analyses. The resultant SOM identified 49 distinct nodes of census tracts .Each SOM node shown in Once SOM nodes are defined, a PCP can be developed to explore the interaction between different environmental and socio-economic risk factors. The PCP shows seven vertical axes representing each of the variables under consideration, and 49 polylines representing clusters of census tracts that are most similar to each other for those seven parameters. Turning to the cluster with census tracts that average the highest environmental risk (mosquito habitat suitability),) this cluster can be visualized with the polyline shown in blue in After finding a significant relationship between environmental variables related to Culex mosquito habitat and the number of dead birds infected with WNV, we examined human incidence rates in California to extract socio-economic data (population demographics related to WNV susceptibility). Our goal was to use geovisualization techniques to explore the combination of both environmental and socio-economic information to better understand this vector borne infectious disease. Out of the very large number of questions that could be explored with geovisualization, we highlighted two specific ones here: (1) what are the characteristics of the California cluster that represents the census tracts with the highest median age, and; (2) what are the characteristics of the California cluster that represents the census tracts with the highest mosquito habitat risk. Many other questions can be explored once the data are extracted, but to illustrate the technique, we will focus on these two questions.For example, the cluster that contains the census tracts with the highest median age (~58 years) can be visualized in the SOM—it is the node highlighted in green and labeled “cluster-1” in The 49 clusters were then analyzed with PCP, allowing visual inspection of the characteristics of the input parameters of each cluster. Cluster-1 (composition includes highest median age) is highlighted as a green polyline in Cluster-2  is highlighted as a blue polyline in n = 19) represent those with the highest median age. The non-contiguous nature of the census tracts associated with this cluster indicates that they are only similar based on their non-spatial attribute characteristics rather than because of geographical location or autocorrelation. In contrast, the cluster that contains census tracts with the highest WNV mosquito habitat risk  tend to be concentrated in geographic space. This spatial insight would be valuable to public health planners who may be planning interventions.Clusters can also be viewed spatially for additional geographic insight. The results from this exploratory analysis suggest that further investigation is required to fully understand the relationship between age and WNV risk. As mentioned above, studies have suggested that elderly people are more vulnerable to WNV, but others such as Additionally, the public health planner may want to explore all clusters represented in Glenn County in order to understand census tracts outside of Glenn County. For example, if the focus is on the only cluster in Glenn County that contains more than one census tract, the planner may want to explore other census tracts outside of Glenn County that are contained in that specific Glenn County cluster. That cluster represents 21 census tracts in the study area see , but theThese examples of geovisualization data mining to explore environmental and socio-economic data related to WNV disease in California represent only a few of the many questions that public health planners may pose. The planners most familiar with the spatial, temporal and historical setting of WNV in California will almost certainly generate different questions. Other infectious diseases in other areas will also generate specific questions to be explored by public health practitioners. Geovisualization will likely provide unique insights.Developing new analytical models that combine environmental and socio-economic model for infectious disease planning is difficult because the data are often collected at differing scales, using differing boundaries, and under differing research contexts, each of which might help explain pieces of an infectious disease independently, but in aggregate may provide much better insight. This article suggests that an exploratory geovisualization process can help planners understand the interplay between environmental and socio-economic data prior to embarking on the difficult development of an analytical model that accounts for these disparities.This study explored the use of geovisualization techniques to uncover patterns in large, complex data sets that would be difficult to otherwise discover. WNV was used as a case study to explore this question—California became the center of United States attention in 2004 and 2005 due to high rate of disease incidence. Geovisualization allowed combining the spatially explicit environmental factors (mosquito habitat risk) with socio-economic data (population demographics) in a data mining context to find previously unknown data clusters at the census tract level. Major challenges for multivariate geospatial mapping include large data volumes, high dimensionality, and the perception of complex patterns . The resWe have shown a few examples of how geovisualization could be used by public health planners to better understand and respond to an infectious disease outbreak. This approach found 1,133 census tracts within our study area of WNV incidence in California, and classified those census tracts into 49 clusters where each cluster contained census tracts that were more similar to each other in terms of WNV environmental and socio-economic parameters, than to the census tracts represented in all other clusters. Examples of several interesting patterns were revealed. For example, the cluster that had census tracts with the highest average mosquito habitat risk only had mid-level median age levels. Had there been a cluster that had both the highest mosquito habitat risk and the highest median age, public health planners might choose more intense intervention measures in those census tracts. Another interesting pattern uncovered was that census tracts in the county that had the highest reported incidence of WNV had relatively low mosquito habitat risk. This might lead to a speculation that demographic and socio-economic parameters should be weighted more importantly than mosquito habitat risk when developing public health plans. Likewise, this pattern might suggest other factors like poor links between modeled mosquito habitat risk and WNV risk in areas outside the training set data or spatial biases in recording effort operating differently at the county level and the census tract level could be at play. Focusing on those ideas through geovisualization may reveal other unknown patterns.This article represents a case study that utilized a retrospective view of a WNV outbreak in California in the mid 2000’s. At that time, geovisualization tools were quite limited and not often used by public health practitioners. Now that the tools are more available, and much easier to use, a future research program that explores using geovisualization in near-real time during an outbreak is appropriate. Infectious disease outbreaks occur frequently, and rapid planning and response are always desirable. Many of these outbreaks are not well understood, and adequate interventions could certainly benefit from data mining, geovisualization approaches. For example, at the time of this writing the Coronavirus (COVID-19) was first reported to the public on 31 December 2019, after the outbreak was first detected in Wuhan City, China . By mid-10.7717/peerj.9577/supp-1Supplemental Information 1This dataset was developed to support research intended to develop a spatially explicit model that explores environmental data related to the risk of exposure to WNV, and the susceptibility to WNV disease based on demographic data of the potentially affected population. The model was developed and then tested on census tracts in an identified 1-standard deviation of WNV incidence in central California. The dataset contains (1) U.S. Census Bureau demographic data for 1,133 census tracks in the ellipse, and (2) and average mosquito habitat risk data for each of those census tracks based upon the model described by West Nile Virus risk based on mosquito habitat model as described in: Kala AK, Tiwari C, Mikler AR and Atkinson SF, 2017, A comparison of least squares regression and geographically weighted regression modeling of West Nile Virus risk based on environmental parameters, PeerJ 5:e3070; DOI 10.7717/peerj.3070Click here for additional data file.10.7717/peerj.9577/supp-2Supplemental Information 2A visual explanation of the plethora of geovisualizations that can uncover patterns in complex data sets.Click here for additional data file.


Student motivational profiles and self-regulated learning strategies are significant influences on overall academic success in university settings. Test anxiety has been repeatedly linked to maladaptive learning strategies and ineffective motivational frameworks. However, the results in the field have been inconsistent with respect to the precise interactions among these variables. This study employed anonymous responses from a group of volunteer students in a mid-sized Midwestern United States university, serving a primarily Caucasian and female population with an average age of 20 years. Adopting a curvilinear analytic design, this study attempted to examine the relationships among these common domains of inquiry into student thriving. The results of this study provide insights examining under which conditions cognitive test anxiety is most likely to be heightened or diminished. The results demonstrated that levels of test anxiety were greatest when (a) learners adopted primarily extrinsic or primarily intrinsic goal orientations, (b) academic tasks where the outcome was uncertain, (c) learners adopted passive learning strategies, and (d) learning strategies were more personally involved . Our results add to the field by identifying curvilinear models provide greater utility in identifying the relationships among these critical emotional and cognitive factors in academic settings. Furthermore, we advocate for employing identification and intervention strategies that recognize individually specific profiles of interactions among test anxiety, motivation, and self-regulation to promote more optimal success in supporting learners in university settings. In contemporary educational settings, the success and thriving of students is of critical concern to university leaders as rates of retention and graduation are challenged across all sectors . With moThese growing trends in the field have largely driven our work, which involves three primary goals in this domain: (a) effectively identify students at-risk for maladaptive affective experiences such as test anxiety, (b) isolate contributing factors that promote deleterious performance due to test anxiety, and (c) promote intervention efforts that can support thriving. Over the course of 25 years of research in this domain, attention to cognitive test anxiety and academic behaviors has repeatedly demonstrated that elevated levels of test anxiety are clearly related to key issues in university success such as motivation and study skills. However, recent trends in the field pointing to moderating variables have sparked renewed interest in reviewing the hypothesis that test anxiety may have some facilitative or functional aspects. To that end, our attention has turned to examining more nuanced relationships among motivation, self-regulation, and test anxiety.Test anxiety influences learners in academic settings through the beliefs, behaviors, and eventual performance differences noted for people with varied levels of experienced affective response in evaluative settings . ResearcThe emotionality component of test anxiety is generally context-specific and is commonly identified through heightened physiological arousal  conceptions for stress and anxiety is the Yerkes-Dodson Law , which pSelf-regulated learning strategies are a collection of cognitive and behavioral strategies that promote optimal success by ensuring that learning activities are strategic and targeted planning, self-monitoring, and effortful control over cognitive processes in directing efforts toward attaining achievement goals . The valFollowing The operationalization of effective SRL in response to stressors can generally be identified by examining the coping strategies learners employ when navigating the stressful or threatening academic context. A key debate in the field centers on the utility of the activation of emotion-focused coping strategies. Several studies have demonstrated that learners who rely on strategies that primarily focus on emotion-focused or avoidance coping are maladaptive when compared to problem-focused (or adaptive) coping strategies , b. HoweClosely related to the concept of self-regulated learning is the broad domain of achievement motivation. There are many viable models of motivation in the academic context that have been used to explain volitional control effectively, including Using this framework, researchers have demonstrated strong relationships among expectancy for success and proactive coping, persistence in the face of failure, academic interest, and performance . While tThere has also been a considerable amount of research examining the influence of achievement goal orientation on learner outcomes . A commoAn alternative (but similar) orientation to explaining goal structures focuses on explaining where the primary drive for achieving the goal comes from, and the underlying affective responses to those differentially sourced goals . ExtrinsOur synthesis of the research on these broad motivational constructs highlights once again the importance of person × context interactions . EssentiWhile the data in this field has consistently demonstrated connections among these motivational and self-regulatory processes and test anxiety, we have noted a considerable gap in the literature regarding the examination of non-linear relationships. That is, primarily linear relationships have been reported in the literature despite the widely known Yerkes-Dodson law of arousal and performance that is established on findings over 100 years old , which an = 298). Participants were enrolled in undergraduate courses in developmental psychology and educational psychology at a mid-sized university in the Midwestern United States. All participants were volunteers, and participation in this survey study was one option of several to satisfy a course requirement. The study protocol and procedures were reviewed and approved as “exempt” by the University Institutional Review Board in accordance with federal guidelines . The nature of the data collection process precluded directly identifying gender, race, or age of the students, but prior studies on this population as well as aggregate demographic data on the classes involved in the recruitment indicate that the sample is predominantly Caucasian, female, average age of 20 years, and from the Midwestern United States, consistent with the programs served by the courses. Analyses of the submitted responses demonstrated that fewer than 1% of the observations contained missing values, therefore listwise deletion was employed.This study examined curvilinear relationships among cognitive test anxiety, self-regulation, and motivation  in a university sample  using unfolding multidimensional scaling model UMSM; . UMSMs aA priori hypotheses for the findings of this study included (a) exploration of the structure of the MSLQ would provide a dimensional solution that supported a new and validated approach to represent student motivation and self-regulated learning; and (b) curvilinear modeling would provide superior fit for explaining the relationships among test anxiety and motivational factors (as measured by the MSLQ) as well as self-regulated learning strategies.Using these four dimensional variables, we explored linear and curvilinear relationships with test anxiety using Generalized Additive Models GAM; . GAMs arThe results of the unfolding model demonstrated that for the motivational variables in the MSLQ  two dimensions were detected . The twoWith respect to the Self-Regulated Learning components of the MSLQ, an unfolding model was also used, revealing two clear dimensions for those variables , represeUsing the four dimensional variables discerned from the unfolding model described above as predictor variables, we explored relationships among cognitive test anxiety and the motivational and self-regulated learning dimensions using Generalized Additive Models (GAMs), with the purpose of ascertaining whether curvilinear functions were present. The resulting GAMs are described below in turn.F = 4.51, p < 0.0001]. The curve in y-axis) were present for individuals who reported predominantly extrinsic or intrinsic goal orientations, as illustrated by the heightened anxiety noted on the outer extreme values on the x-axis. Reported cognitive test anxiety was lowest for individuals with more even mixtures of goal orientations (mid-level on the x-axis). More specifically, the lowest reliably predicted point of anxiety occurs when the mixture between extrinsic and intrinsic goal orientation tendencies slightly favor the intrinsic category. The curvilinear relationship for GO explained approximately 10% of the variance in reported test anxiety.The results of the GAM exploring the relationship between CTA and the GO dimensional variable demonstrated a statistically significant curvilinear relationship between GO and CTA , representing an “inverted U” pattern. Test anxiety (y-axis) was highest when expected success (x-axis) was just below the center point, which is more precisely conveyed as the point of greatest uncertainty. Conversely, lower levels of test anxiety were noted when performance outcomes were at either extreme of the x-axis. As such, when success predictions were highly certain (either for success or failure), students reported the lowest degree of cognitive test anxiety. This relationship explained approximately 11% of the variance in test anxiety.The GAM curve for the model relating CTA and the Expectancy dimension appears in F = 11.62, p < 0.0001]. As can be seen in There was not a statistically significant curvilinear relationship between cognitive test anxiety and the Active Learning dimension. However, there was a significant inverse linear relationship between these two variables . x-axis were associated with lower levels of cognitive test anxiety. In practice, this means that students with a greater reliance or use of socially oriented learning activities tended to have lower levels of CTA. As levels of PRL increased, the level of reported cognitive test anxiety also increased, but overall values of CTA were moderate and stable overall after the first increase noted. This result suggests that above a certain threshold of using socially engaged strategies  there was not a clear direct relationship between PRL and elevated levels test anxiety. However, below that threshold cognitive test anxiety was relatively lower, suggesting that higher reliance on the low-personally responsible activities  or low use of self-regulated strategies high on the PRL dimension are associated with lower test anxiety. This non-linear relationship accounted for approximately 5% of the variation in cognitive test anxiety.The final dimension yielded by the multidimensional scaling, Personal Responsibility for Learning (PRL), had a statistically significant curvilinear relationship with CTA [The results of this study provided several insights to the fields of test anxiety and learner outcomes, both in theoretical and practical domains. First, the use of multidimensional scaling to review the data from the MSLQ provides a new approach to represent this commonly used scale in the literature, offering an alternative representation to examine motivational and self-regulatory constructs measured on Over the last 30 years, several attempts to identify a factorial structure that is universally accepted for the MSLQ have been offered. No clear consensus has been achieved in these efforts, leaving most researchers to rely upon ill-fitting solutions or referring to the separate subscales specifically in their investigations. Composite scores for the Motivation domain items separate from the Self-Regulated Learning domain are often used, but typically preclude strong theoretical tests because the subscales that form the composite scores often represent contradictory orientations that do not combine clearly to create an overall “motivation” score . The dimensional variables that were articulated in the multidimensional scaling analyses provided remarkably clear theoretical consistency with the overall intent of the MSLQ, as reported by The analysis of the motivational variables used in this analysis revealed a clear two-dimensional representation for the five constructs measured in the MSLQ, providing what we interpreted as a Goal Orientation axis and an Expectancy axis. These two broad constructs are encompassed within all leading theories of motivation, and suggest that the MSLQ motivation scales can inform understanding of motivational goal theories e.g.,  as well Our primary goal in this study was to expand the literature by exploring the potential for better identifying the relationships among test anxiety and motivational and self-regulated learning constructs. While the data provide clear evidence of curvilinear effects, it’s important to clarify that traditional methods employed with this data set would have generated significant values and meaningful results. Simple linear effects were significant, but inferior overall in the reported outcomes. Striking a regression line for the “best fit” on a linear scale would indeed generate a line, but the nuance missed that is seen with curvilinear analyses would have masked the true relationships. We anticipate that examination of curvilinear effects in future studies on test anxiety (and related measures) will provide greater precision in isolating the debilitating and facilitative effects of emotional constructs within the academic arenas.The significant curvilinear relationship between Goal Orientation and Test Anxiety indicated heightened levels of test anxiety for students who tended to report extreme levels of extrinsic or intrinsic goal orientations. When there were more moderate levels of GO, which indicates a mixed representation for extrinsic and intrinsic goals – as well as a high “task value” orientation in the MDSM – cognitive test anxiety levels were lowest. Compared to recent work with goal orientation that generally indicates that performance goals tend to be overly maladaptive e.g., , these fThe significant “inverted U” pattern observed between Expectancy for Success and Cognitive Test Anxiety was reminiscent of the classic Yerkes-Dodson curve (1908). In this case, the curvilinear relationship exposes an intimate relationship between cognitive test anxiety and uncertainty. When learners are able to reliably predict their performance outcomes, the degree of anxiety was suppressed. Overall, this finding is the most significant theoretical contribution to the test anxiety literature, lending confirmatory empirical evidence to multiple theoretical accounts for test anxiety simultaneously by examining the data in this non-linear approach. In addition, it provides an advancement in the explanation of how these various representations may be fostered and lead to sub-optimal performances.The value of this curvilinear analysis is the isolation on the key issue in the expectancy–test anxiety relationship. Traditional representations examining linear models conclude that high self-efficacy is associated with lower test anxiety. However, the data in this model suggests that relationship may have been misleading. While it is true that higher self-efficacy tends to be related to higher performance – and lower test anxiety – the data in this case suggest that when it comes to test anxiety, low levels of self-efficacy would not necessarily produce high degrees of anxiety. What has been most clear in these analyses is that the critical issue that tends to drive anxiety upward most readily is a sense of uncertainty or low confidence levels in predicting outcomes. In this way, we see a connection with the findings demonstrating that low levels of perceived control or predicted outcomes for forthcoming evaluative events are likely to be associated with increased test anxiety e.g., .A weak statistically significant effect was identified for the Personal Responsibility dimension, which identifies the degree to which learning behaviors or study skills require independent personal engagement. Cognitive test anxiety increased, then plateaued after reaching a critical level of personal responsibility, indicating that those learners who infrequently endorse engaging in individually driven learning activities or frequently employed externalized learning support activities had the lowest levels of cognitive test anxiety. This finding may demonstrate that socially dependent learning strategies provide an “escape” function that allows release from anxiety, consistent with The plateau effect observed with test anxiety once level of personal responsibility reached a critical level may better explain previous findings related to test anxiety and study strategies. There has been ample evidence that students with high-test anxiety engage in various study support strategies, but the divergence when compared to low-test anxious learners is in the quality of chosen strategies . StudentThere was no curvilinear pattern observed between cognitive test anxiety and the Active Learning dimensional variable. Rather, the data demonstrated a clear and strong inverse linear relationship, demonstrating that as learners were more likely to endorse active learning behaviors, their degree of test anxiety declined. This pattern is once again consistent with research identifying students with high levels of test anxiety are more likely to engage in passive or avoidant coping strategies  where they struggle. That is, we advocate for examining learners’ perceptions of the events (degree of perceived threat and perceived competence to succeed), reviewing the goal structures that have been adopted for the learning event, and identifying specific coping strategies that support optimal performance in meeting those situation-specific goals. While our results demonstrate trend data suggesting that anxiety will peak in situations where the outcome is uncertain, passive learning strategies are adopted, or polarized goal orientations are adopted, truly effective interventions for individual learners will only be achieved when unique profiles are examined and addressed (see While the results of this study clearly supported our initial hypotheses that examining relationships among motivational and self-regulated learning dimensions measured by the MSLQ would be better achieved with attention to curvilinear relationships, there are limitations to the current study that require attention in future studies. First, the conditions of our data collection method in this study precluded specific demographic information from individual participants. We know that future studies will add to this literature with the ability to examine differential patterns based on gender, race, prior academic skills, and other variables in the field that have proven instructive. Second, the current study resulted in a dynamic solution for the dimensional scaling of the MSLQ. Repeated examinations with the MSLQ have revealed several competing explanations for representing the structure of the underlying constructs, and our study adds to that list. However, the dimensional analysis results would need to be re-run in future studies to estimate those representations of the dimensions in the MSLQ, as there is no simple translation generated in this procedure. Continued attention to a dimensional representation of the MSLQ is recommended to provide an alternative solution for representing motivation and self-regulated learning in students. Finally, the population from which this sample was drawn was a predominantly female, Caucasian, and from the Midwestern United States. Continued examination of these constructs in more diverse samples would be important for greater generalization.The datasets generated for this study will not be made publicly available due to IRB restrictions. Requests to access the datasets should be directed to the corresponding author.The studies involving human participants were reviewed and approved by the Ball State University Institutional Review Board. The ethics committee waived the requirement of written informed consent for participation.JC conceptualized the data collection process, collected the data, and initiated the interpretation of the findings related to the field. WF conceptualized the analytic approach, conducted all analyses, and collaborated on the interpretation of the findings. JC and WF agreed to the order of authorship but also acknowledge the work was a true collaboration and equitable worth of both authors’ contributions is asserted.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The development of cervical cancer has been shown to involve both viral and host factors. The host factors are those that determine the specific response to human papillomavirus (HPV) infection by the patient's immune system. The immune responses to vaccines have been shown to be influenced by polymorphisms in genes involved in innate and adaptive immunity. The specific genetic variants that may influence the immune responses to HPV vaccine which may contribute to persistence of immunity (POI) have not been widely studied yet. In order to address the question as to “is it right to vaccinate all children, and all with equal dose?” we have critically examined the knowledge of common immunogenetic and immunogenomic variations that may influence the HPV vaccine POI across various populations. We have also identified a number of specific research questions that need to be addressed in future research into host molecular genetic variations and HPV vaccine POI in order to afford life-long protection against the development of cervical cancer. This work informs future insights for improved HPV vaccine designs based on common host molecular genetic variations. Haemophilus influenzae type b (Hib) and Neisseria meningitidis alone, are estimated to cause 340,000 episodes of severe Hib in the year 2015, and there are about 500,000–1,200,000 invasive meningococcal diseases occur each year worldwide  molecules, cytokines, and cytokine receptors, have been associated with heterogeneity of immune responses to a wide range of vaccines , who do not develop cervical neoplasia can help to assess individual genetic factors for natural immunity to HPV persistent infection .Older women with a persistent oncogenic HPV infection who do not develop cervical neoplasia can be examined to assess individual genetic factors for natural immunity to HPV persistent infection .Given the complexity in molecular genetic variations of the host and immune responses to HPV vaccine, future efforts will likely require the use of high-throughput genetic technologies, such as WES, to assess the polymorphisms of numerous immune response genes simultaneously, in a timely and accurate manner. This will enable comprehensive assessment of personalized host immune gene pathways rather than a limited number of candidate genes or markers.Molecular genetic variations exert distinct influences on antigen-specific immune responses , 32. The*15/HLA-DQB1*0602/HLA-DQA1*0102,HLA-B*0702/HLA-C*0702, and HLA-DRB1*0401/HLA-DQA1*0301, were associated with increased risk for cervical neoplasia” after HPV infection.However, the identification of causal variants within the HLA region is intrinsically difficult due to the complex polymorphisms found in this region. During HPV vaccination, the early immune response signals originate from differing abilities of classical HLA alleles to bind virus-like particles (VLPs). The HLA genes, through innate immunity, play an important role in the initiation process of producing antibodies against viral infection . HLA-assHLA class I and HLA class II) as endogenous or exogenous antigens, respectively (In innate immunity antigen-presenting cells (APCs), such as dendritic cells, internalize the HPV vaccine VLPs and display them on their surfaces with the help of major histocompatibility complex (MHC) genes  above, it is unclear how this would relate to other ethnic groups in Sub-Saharan Africa , and worm infestations may affect the effectiveness of vaccines  above, the genetic differences between populations may change the incidence and severity of vaccine adverse events (What are the acute and chronic adverse events of HPV vaccination according to different HPV vaccine dosage regimens in different populations? (Common vs. uncommon adverse events). e events .Rationale: Host molecular genetic variations in the same population or between different populations may have a profound effect on the vaccine effectiveness  of women developed persistence of oncogenic HPV infection and later cervical precancerous lesions , 65. AppAlthough several environmental risk factors have been demonstrated to influence the immune responses to vaccines, genetic factors are the major determinants . For exaHLA restrictions. To quote Mentzer et al. (Identification of the non-HLA genes influencing HPV antibody responses in vaccinated women should aid the development of novel and, improved HPV vaccine, and its adjuvants that act independently of the r et al. ; “if a gHLA alleles (It is therefore important that individuals who have experienced primary or secondary HPV vaccine failures should be well-examined in order to determine whether they also possess different allele frequencies of the risk or protective  alleles . If suchImplementing molecular genetic profiling or WGS at a national level may or may not be practical or economical for now, but it may certainly become ethically compelling to target at-risk individuals or population groups in the future in order to bring about the prospect of personalized HPV vaccination. The preventative healthcare strategies may involve universal genotyping of new-borns on a national scale and this may become a reality in the future in an effort to predict disease genetic risk , 59. SucNovel methods of creating vaccine adjuvants with personalized vaccination based on molecular genetic variations, may become useful strategies in settings, where vaccine immune responses are suboptimal resulting in a raised incidence of HPV vaccine failure. A previous study carried out in the UK and the Netherlands by O'Connor et al. , on threHPV vaccine failures can be studied further by genotyping via WGS or WES. As suggested by O'Connor et al. ; “NGS apHLA, would benefit from scientific approaches that move beyond individual allele–disease associations to investigate biologically relevant groups. It is hoped that the identification of genetic markers for HPV vaccine immune responses and the POI could soon prove their clinical utility because the costs for WGS and WES are progressively decreasing.Longer-term surveillance in a vaccinated population is needed to identify waning immunity, and to evaluate requirements for booster immunizations to improve HPV vaccine effectiveness. Future genetic studies with respect to Host molecular genetic variations undoubtedly play a major role in HPV vaccine immune responses and the POI. Assessment of an individual's immunogenetic and immunogenomic backgrounds is crucial for the evaluation of HPV vaccine long-term immunogenicity, effectiveness, and safety. Molecular genetic profiling for variations in common immune response genes will improve HPV vaccine POI by enabling targeted vaccination according to individual molecular genetics in a more cost-effective manner. However, it is essential that studies investigating the contribution of host molecular genetic variations to HPV vaccine POI are; carefully planned, interpreted with caution, and account for known and unknown environmental factors. The results of these studies should help us to understand how we can design an improved HPV vaccine that protects every child individually, and only administered to those children who are at higher risk. The ability of current HPV vaccine to generate effective lifetime vaccine-induced immunity requires further research in different populations. Our work adds novel insights to the future of HPV vaccine designs based on individualized genetic risk, common host molecular genetic variations, and HPV vaccine POI in different populations.RC drafted the manuscript, EK and GR contributed equally to the editing and completion of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Neuroendocrine influences on cognitive (dys)function are well recognized. In particular, hormones related to the stress axis, the Hypothalamic–Pituitary–Adrenal (HPA) and the sex hormone axis, the Hypothalamic–Pituitary–Gonadal (HPG) are receiving growing attention in relation to their role in the development and manifestation of several psychiatric and neurological diseases that involve cognitive disruption . For exaThe menstrual cycle is a model of hormonal flux that has garnered interest in understanding endogenous sex hormone influences on cognitive functioning and emotion processing. Premenstrual mood disorders, including premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD) have significant psychosocial impacts in the week(s) leading up to menstruation , but theMoving from endogenous to exogenous sex hormones, in this special issue Gurvich et al.  examine Sex-steroid hormones are thought to influence brain, behaviour and cognition via genomic and non-genomic actions although a complete understanding of the mechanisms by which sex-steroid hormones influence cognition is lacking. One potential mode of action could involve cholinergic neurotransmission, and this is explored in this special issue by Ch’ing et al. . Their sThis special issue also explores stress hormone influences on cognition. Two articles are included that examine work stress  and streIn an older adult population, Condello et al.  investigWe hope that the topics addressed in this special issue will continue to stimulate research into the neuroendocrinology of cognition, particularly the role of stress and sex hormones.


Internationally there is pressure to contain costs due to rising numbers of hospital admissions. Alongside age, socioeconomic disadvantage is the strongest risk factor for avoidable hospital admission. This equity-focussed systematic review is required for policymakers to understand what has been shown to work to reduce inequalities in hospital admissions, what does not work and where the current gaps in the evidence-base are.th February 2020 in any language will be included. Electronic searches will include MEDLINE, Embase, CINAHL, Cochrane CENTRAL and the Web of Knowledge platform. Electronic searches will be supplemented with full citation searches of included studies, website searches and retrieval of relevant unpublished information. Study inclusion, data extraction and quality appraisal will be conducted by two reviewers. Narrative synthesis will be conducted and also meta-analysis where possible. The main analysis will examine the effectiveness of interventions at reducing socioeconomic inequalities in hospital admissions. Interventions will be characterised by their domain of action and approach to addressing inequalities. For included studies, contextual information on where, for whom and how these interventions are organised, implemented and delivered will be examined where possible.An initial framework shows how interventions are hypothesised to reduce socioeconomic inequalities in avoidable hospital admissions. Studies will be included if the intervention focusses exclusively on socioeconomically disadvantaged populations or if the study reports differential effects by socioeconomic status  with respect to hospital admission or readmission . Studies involving individuals of any age, undertaken in OECD (Organisation for Economic Co-operation and Development) countries, published from 2000 to 29Ethical approval was not required for this protocol. The research will be disseminated via peer-reviewed publication, conferences and an open-access policy-orientated paper.CRD42019153666. The study employs a rigorous international gold-standard methodology ) to undertake an equity-focussed systematic review and therefore will provide a comprehensive overview of existing literature on interventions that reduce socioeconomic inequalities in avoidable hospital admissions.By following PRISMA-E guidelines this study includes a framework to explain how interventions might reduce inequalities in hospital admissions which will be useful to decision-makers.The review adopts a newly developed and validated comprehensive search strategy for identifying studies focussed on equity issues and is therefore at the forefront of developments in equity-based systematic reviewing practice.One limitation is that we will not conduct full-text hand-searching and so may miss studies which have not made any reference to equity-related terms in either the title or abstract.Another limitation is that studies may be too heterogeneous to obtain combined effect estimates through meta-analysis.Due to pervasive socioeconomic inequalities in health,7Different ethnic groups experience different avoidable admission rates, and the interrelationship between ethnicity and socioeconomic status is complex and context specific.There is growing pressure to contain costs and prevent disruptions in elective hospital care due to a rising number of hospital admissions across OECD countries.18Some countries have explicit statutory duties to address health and healthcare inequalities. In Spain, the Royal Decree Law 7/2018,23only the effects of interventions on reducing levels of hospital admissions overall, as opposed to the effects on inequalities in avoidable hospital admissions. The differential effectiveness of interventions across socioeconomic groups in unknown, including a lack of evidence on any interventions aimed at reducing inequalities or targeted in their delivery to disadvantaged groups.4 13There are promising interventions to reduce avoidable hospital admissions in areas including education, self-management, rehabilitation and telemedicine.It is critical for policy-making in this area that evidence of the effectiveness of different types of interventions at tackling inequalities is systematically reviewed. There is currently a lack of accessible policy and practice ready evidence on what works in terms of interventions to reduce inequalities in avoidable hospital admissions. Conversely, there is limited understanding of what interventions should be avoided; frequently well-intentioned interventions may increase rather than decrease inequalities.28Against this backdrop, this systematic review will address this deficit in the knowledge base by reviewing primary studies of the effectiveness of interventions in reducing socioeconomic inequalities in avoidable admissions. It will consider interventions  which might reduce socioeconomic inequalities in avoidable admissions, highlight any gaps in the evidence base and, for any interventions identified, seek to establish how such interventions are organised, implemented and delivered.In line with PRISMA-ETackling socioeconomic inequality in healthcare is challenging,Population health and policy-level interventions – legal, fiscal, structural, organisational, environmental and policy interventions that seek to change health-related behaviours or to modify the social and economic determinants of health.48Community-based interventions – community resources and assets, for example, local area coordination initiatives.Health and social care service-based interventions – in this context these might include interventions considered in previous reviews of avoidable admissionsIntegrative interventions – interventions that create greater synergy and closer working between domains 1 to 3 or within 3 . For example, social prescribing initiatives facilitate closer working between domains 2 and 3.et alAccording to Benach Targeted intervention to those in most disadvantaged socioeconomic group only, focussed on closing the health gap (selective: gap focussed).Universal intervention, with additional focus on those in the most disadvantaged socioeconomic group to close the health gap .Redistributive intervention, a universally applied intervention, but where the extent of benefit increases across the social gradient, such that the most socioeconomically advantaged are not expected to benefit at all from the intervention due to the lack of need for it (selective: gradient focussed).Proportionate universalism, universal intervention benefitting all, but with increasing benefits of intervention across the social gradient .Here, we provide three hypothetical illustrative examples of the application of this framework in the context of reducing socioeconomic inequalities in avoidable hospital admissions. First, there may be a social prescribing intervention (domains 2 and 4) which is facilitated through citizens advice services and serves those who have a limiting long-term condition and are in receipt of unemployment or other welfare benefits (approach c). This intervention is designed, financed and implemented in such a way that it is successful in reducing avoidable hospital admissions for these individuals. Second, there may be a water fluoridation intervention (domain 1). This intervention benefits the whole population in terms of reductions in hospitalisations resulting from tooth decay but with increasing benefits across the social gradient as need for the intervention is greatest in lower socioeconomic groups where tooth health is poorest (approach d). Frequently well-intentioned interventions may increase rather than decrease inequalities.The review will be carried out following established criteria for the good conduct and reporting of equity-focussed systematic reviews using PRISMA-E guidelines10.1136/bmjopen-2019-035429.supp1Supplementary datast October 2019. The review is planned to commence in March 2020 and is anticipated to take 1 year to complete.Preliminary searches were carried out in September and October 2019 and the study registered with the PROSPERO International Prospective Register of Systematic ReviewsAn advisory panel comprising collaborators from the English public health and healthcare system, national and international research communities, social and voluntary sector and patient and public representatives will guide the research. The early engagement of patients and members of the public through the National Institute for Health Research (NIHR) North East Research Design Service consumer panel at the stage of requesting NIHR funding to carry out this research has been instrumental in shaping this systematic review protocol. Patient and public involvement (PPI) members helped to strengthen the articulation of the study rationale. We received a powerful mandate from panel members to undertake the research based on their unanimous belief about its critical importance to the healthcare system and wider society. It became apparent during our discussions that the legal commitment to reducing inequality in service provision and outcome in England was not widespread public knowledge and panel members suggested this was given more prominence in the background rationale.Through the collaborator advisory panel, patients and the public will contribute, alongside academics, policy and practice partners, to the iterative further development of the framework . In ordeWhat interventions  reduce socioeconomic inequalities in avoidable hospital admissions?What interventions  maintain or increase socioeconomic inequalities in avoidable hospital admissions?This study has two objectives:To systematically review the effectiveness of interventions  in reducing socioeconomic inequalities in avoidable hospital admissions.For interventions identified through the systematic review, establish where possible, how these specific interventions are organised, implemented and delivered.Studies will be selected according to the criteria outlined in The review will examine interventions across the range of domains of action  which might reduce socioeconomic inequalities in avoidable hospital admissions in individuals of any age. It will also uncover those interventions which may serve to maintain or increase inequalities.The review will use the intervention framework and group interventions by their domain of action and by their approach to tackling inequalities . Where p‘Avoidable admissions’ are those for which timely and effective ambulatory care can prevent the need for hospitalisation.The search will be limited to studies published in the last 20 years (since 2000) that have been carried out in an OECD country to ensure contemporaneity and a degree of commonality in health system maturity and socioeconomic and demographic context. We recognise that OECD countries will have different characteristics including different health systems and socioeconomic situations and we will interpret the interventions that we find taking into account this variation in context.A rigorous and inclusive international literature search for experimental, quasi-experimental and analytical observational studies will be conducted. This will include randomised and non-randomised controlled trials, non-controlled trials, prospective and retrospective cohort studies (with and/or without control groups), time series data studies, panel studies (with and/or with control groups), case-control studies and other ecological studies (for example studies using administrative data) of the effectiveness of interventions at reducing inequalities in avoidable admissions to hospital. In the case of non-randomised controlled trial studies the authors will make a judgement as to whether any association reported may be considered causal, taking into account the type of study undertaken and following a thorough assessment of study quality and risk of bias (see risk of bias section below).Published studies in any language meeting the inclusion criteria will be sought by searching peer-reviewed literature in electronic databases including (host sites given in parentheses): MEDLINE (Ovid), Embase (Ovid), CINAHL (EBSCO), Cochrane CENTRAL (Wiley), Science Citation Index (Web of Knowledge), Social Science Citation Index (Web of Knowledge), Conference Proceedings Citation Index – Science (Web of Knowledge), Conference Proceedings Citation Index – Social Science (Web of Knowledge). All searches will be re-run prior to final analyses and any further studies identified retrieved for inclusion. Please see 10.1136/bmjopen-2019-035429.supp2Supplementary dataWeb of Knowledge Citation Indices will be used to undertake full citation searches of included studies. Electronic database searches will be supplemented with website searches, included but not restricted to the Kings Fund and the Nuffield Trust. We will request relevant information on unpublished and in-progress research from key experts in the field, identified through the collaborator network for this systematic review.All articles identified through the search will be uploaded into EPPI-Reviewer 4 software.Data for included studies will be extracted by two reviewers using a bespoke form within EPPI-Reviewer4 software.10.1136/bmjopen-2019-035429.supp3Supplementary dataFor included studies, data on the organisation, implementation and delivery of these interventions will be sought from the manuscript and any associated papers through a linked citation search. In conjunction with an OECD healthcare system typology,The methodological quality of the included studies will be appraised independently by two reviewers using the Effective Public Health Practice Project (EPHPP) tool,Data will be presented narratively as well as in tables (where possible) to describe the study designs, population and findings and to address each of the research questions. The main analysis will examine the effects of interventions across the domains of action on socioeconomic inequalities in avoidable hospital admission, categorising the approach adopted to address inequalities for each intervention, using the multidimensional framework outlined in Where applicable and feasible, we will synthesise results for different population subgroups separately  where the differential impact of interventions across socioeconomic groups on hospitalisations was considered.29–31The review will consider interventions which reduce socioeconomic inequalities in avoidable admission to hospital. The review will also serve as a mapping exercise of the types of interventions that have been evaluated in relation to tackling inequalities in avoidable admissions, thereby highlighting any gaps in the evidence base.For included studies, further scrutiny of any published information about them, will seek to uncover how these interventions are organised, implemented and delivered. Context is increasingly recognised as important; real-world interventions are rarely implemented in isolation and the complex interaction of interventions with the particular social context in which they are embedded determines outcomes.The study design inclusion criteria in the review are broad, given that while trials of service-based (domain 3), and even community-based (domain 2) interventions may be likely, we expect a lack of experimental studies in relation to population health and policy-level (domain 1) and many integrative interventions (domain 4) given these tend not to be easily or routinely evaluated using experimental study designs.41 60Only a minority of intervention studies published report any outcome by a social determinant of health and locating these studies has previously been hampered by the absence of validated equity search filters.Our extensive search strategy, combined with the inclusive study design criteria, will ensure that a sizeable literature will be located for synthesis. A 2012 series of reviews of interventions to reduce unplanned hospital admissions found 1530 controlled trials.Ethical approval is not required for this protocol or for the systematic review because it does not involve primary data collection.We anticipate the findings for this review will contribute to an improved understanding of interventions which reduce socioeconomic inequalities in avoidable hospital admissions. Once the evidence has been synthesised a workshop will be held with invited health and social care commissioners and providers, the public health practice community who hold responsibilities for reducing health and healthcare inequalities; relevant social, voluntary and charitable sector organisations; patient and public representatives as well as national and international research community representatives to discuss the results, further refine the framework for action, aid the write-up of the study findings and facilitate the translation of the findings into practice. The research will be disseminated via national and international academic and practitioner cross-over conferences, and a paper submitted to a leading journal in this field. Furthermore, a policy-orientated summary paper will be published on an open access basis so that it is freely available to practitioners and the public.


Nature Communications are eager to continue our engagement in this exciting and rapidly evolving field.Computational chemistry methods with an optimal balance between predictive accuracy and computational cost hold major promise for accelerating the discovery of new molecules and materials. We at  Modelling can significantly help to bridge the results of fundamental materials research to actual materials production by significantly reducing timescales. The computational chemistry approaches developed over the years have been an invaluable tool to provide deep insight into chemical processes beyond what can be directly measured experimentally. A new Collection [Machine learning for chemical discovery1.For many years, density functional theory (DFT) was considered the method of choice to study the electronic structure of molecules, materials and condensed systems, enabling an optimal trade-off between accuracy and computational cost. This balance could be achieved by including the complex many-body electron–electron interactions within a functional of the density, i.e. the exchange and correlation functional. During the 1980s and 1990s, thei key to the huge advances achieved by molecular simulations was to develop more and more accurate quantum-mechanical approximations in order to climb the so-called Jacob’s ladder, with each rung representing increasing levels of complexity and decreasing levels of approximation to the exact exchange and correlation functional. This led to the so-called chemical modelling revolution, as highlighted by Tkatchenko in his Comment entitled Considering how the world has changed with the increasing availability of curated datasets containing reliable quantum-mechanical properties of molecules and materials, and how our ability to collect big data has greatly surpassed our capability to analyze it, a completely different strategy is to think about how seemingly unrelated data and properties may impact each other, studying the hidden interconnections between them. In this vein, an alternative approach to advance the predictive capability of computational approaches is to replace the physically motivated path by a data-driven search. This has given rise to big-data-driven science, which applies machine learning (ML) techniques to molecular and materials science. While ML approaches have been in use for decades for identifying correlations from big amounts of data, only recently has the computational community started to invest tremendously in programme infrastructures based on the synergetic collaboration between materials scientists, who have experimental and theoretical expertise, and computer scientists to develop ML methods aimed at discovering new molecules and materials. Under development are ML methodologies that combine electronic structure calculations and statistical analysis tools, which when fed with increasingly available molecular big data, can serve as alternatives to standard methods to explore the vast chemical space. In these ongoing efforts, the computational community currently faces theoretical and technical challenges.Computational studies of chemical processes taking place over extended size and time scales must balance computational cost and accuracy: electronic structure methods are very accurate but computationally expensive, while atomistic models such as force fields—although computationally affordable—lack transferability to new systems.Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning, Smith et al.2 discuss that an ideal solution to achieve the best of both approaches lies in developing a general purpose neural network potential that approaches CCSD(T) accuracy , the gold standard in quantum chemistry, yet exhibits transferability over a broad chemical space. Most importantly for practical calculations, the resulting potential is an attractive alternative to DFT approaches and standard force fields: it is broadly applicable for conformational searches, molecular dynamics, and the calculation of reaction energies and is billions of times faster than CCSD(T) calculations.In Within traditional DFT modelling, seeking to increase the non-locality of the exchange and correlation functional in the effort to achieve more accurate approximations comes at a steep increase in computational cost, making related computational efforts impractical. A different approach in this area is to develop specialized ML functionals, whose overall accuracy does not significantly degrade when used outside their training scope.Machine learning accurate exchange and correlation functionals of the electronic density tackle this problem by introducing a fully machine-learned functional that depends explicitly on the electronic density and implicitly on the atomic positions3. It approaches the accuracy of high level quantum chemistry methods at an affordable computational cost. Although these functionals were created for a specific dataset and hence are not universal, they exhibit promising transferability from the gas to condensed phase and from small to larger molecules within the same type of chemical bonding.Dick and Fernandez-Serra in Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions4. The deep learning approach introduced by these authors provides full access to the electronic properties needed for practical calculations of reactive chemistry, such as charge populations, bond orders, and dipole and quadrupole moments, at a force-field-like efficiency. Moreover, the approach may enable property-driven chemical structure exploration, suggesting promise towards inverse-chemical design.One common feature of machine learning approaches used in molecular simulations is that since the electronic properties are learned from quantum chemistry data, each individual model is typically limited to exploring these specific properties. Since all the physical and chemical features of a hypothetical compound can be derived by its ground-state electronic wavefunction, one way to circumvent this problem is to establish a direct link between ML and quantum chemistry with a ML model that predicts the ground-state wavefunction, as discussed by Schütt et al. in 5 in The electronic structure of benzene from a tiling of the correlated 126-dimensional wavefunction. Using high-level correlated wavefunction theory, the authors revisit the electronic structure of benzene, which has been a test bed for competing theories throughout the years. In alternative to the traditional description of the electronic structure in terms of molecular orbital (MO) theory, the authors rely on a method to identify and visualise wavefunction tiles, known as dynamic Voronoi Metropolis sampling. The use of such high-level theory enables them to reveal the fundamental effect of electron correlation in benzene and show its manifestation in the preference for staggered Kekulé structures, whereas the interpretation of electronic structure in terms of MO theory ignores that the wavefunction is anti-symmetric upon interchange of like-spins.Although acknowledging the rapid evolution of computational techniques is exciting, this is not to suggest that traditional deep quantum chemistry expertise is obsolete: on the contrary, standard high-level theoretical approaches are still indispensable for solving fundamental problems in computational chemistry. A nice example is shown by Liu et al.Automated extraction of chemical synthesis actions from experimental procedures, Vaucher et al.6 make a first important step towards implementing the automated execution of arbitrary reactions with robotic systems by developing a deep-learning model that performs the conversion of chemical instructions for organic synthesis reactions.ML algorithms and natural language processing approaches also offer new possibilities in optimizing and automating reaction procedures. On-demand synthesis of small drugs is of key interest in this area, where both the forward synthesis  and the retrosynthesis  can strongly benefit from recent modelling advances. Reaction predictions are usually considered a machine translation problem between simplified molecular-input line-entry system (SMILES) strings (a text-based representation) of reactants, reagents, and the products. The ultimate goal is to implement human-refined chemical recipe files to feed a robotic platform, which then execute the actual synthesis in an automated manner. A challenge here revolves around the need to extract chemical instructions from patents and the scientific literature, where they are reported in prose, and convert them to a machine-readable format. In Although data-driven computational approaches clearly hold promise towards speeding up the discovery of new molecules and materials, at the moment current applications are only at the beginning of the exploration phase. The reliability of any ML approach depends on the availability of extensive datasets for model training, the bottleneck in cases where data is not abundant or difficult to generate. Along with the need for extensive curated data sets of microscopic and macroscopic molecular properties, future work should target the development of more transferable models with universal approximations that can treat local chemical bonding and non-local interactions on the same foot.Designing and understanding light-harvesting devices with machine learning7.As an ultimate goal, the hope is to develop ML approaches that can not only provide predictive models but also interpretable models to stimulate the formation of novel scientific concepts and deeper understanding of a given research field, as Häse et al. suggest in their Perspective piece Nature Communications are eager to continue our contribution to this exciting and fast-developing field. While we acknowledge the importance of standard high-level computational frameworks, we recognize the tremendous potential of data-driven ML schemes towards accelerating the discovery of material systems with target properties. We strongly believe that a synergistic effort across disciplines—involving computational chemists, computer scientists, experimental chemists and material scientists—will play a crucial role for enhancing the rational design of new molecules and materials.We at “While we acknowledge the importance of standard high-level computational frameworks, we recognize the tremendous potential of data-driven ML schemes towards accelerating the discovery of material systems with target properties.”


Pore-forming toxins are alluring tools for delivering biologically-active, impermeable cargoes to intracellular environments by introducing large conductance pathways into cell membranes. However, the lack of regulation often leads to the dissipation of electrical and chemical gradients, which might significantly affect the viability of cells under scrutiny. To mitigate these problems, we explored the use of lysenin channels to reversibly control the barrier function of natural and artificial lipid membrane systems by controlling the lysenin’s transport properties. We employed artificial membranes and electrophysiology measurements in order to identify the influence of labels and media on the lysenin channel’s conductance. Two cell culture models: Jurkat cells in suspension and adherent ATDC5 cells were utilized to demonstrate that lysenin channels may provide temporary cytosol access to membrane non-permeant propidium iodide and phalloidin. Permeability and cell viability were assessed by fluorescence spectroscopy and microscopy. Membrane resealing by chitosan or specific media addition proved to be an effective way of maintaining cellular viability. In addition, we loaded non-permeant dyes into liposomes via lysenin channels by controlling their conducting state with multivalent metal cations. The improved control over membrane permeability might prove fruitful for a large variety of biological or biomedical applications that require only temporary, non-destructive access to the inner environment enclosed by natural and artificial membranes. ChlorofImage recording and analysis was performed with a Nikon TS100 microscope  in transmission/epifluorescence mode, which was equipped with an Infinity camera  and appropriate filters. After lysenin addition, trivalent metal ion removal was performed by precipitation upon the addition of a sodium phosphate solution  that was


Atheendar S. Venkataramani and colleagues discuss economic factors and population health in the United States. The United States is in the midst of a 40-year-long population health crisis. Life expectancy has declined since 2014, an unprecedented event that has followed on the heels of a decades-long slowing in secular gains in longevity in the US relative to peer countries. These adverse population health trends appear to be primarily driven by worsening health among working-age individuals of lower socioeconomic status.A growing body of research suggests that worsening economic outcomes—e.g., fading employment opportunities and increasing economic insecurity—may be a primary causal driver of adverse health trends among low-income and less-educated working-age US residents.Evidence-based public policies to address widening gaps in economic and health outcomes include expanding early childhood health and educational investments, increasing the scope of programs that assist displaced workers in developing new skills and finding new jobs, reinforcing the social safety net, and improving the reach of public health efforts to help moderate the health consequences of adverse economic shocks.Policymakers will also need to consider and rigorously evaluate new approaches, such as basic income grants, investments to direct automation toward complementing rather than replacing the work force, or job guarantee programs.The size and scope of the population health challenges that have arisen with the changing economy highlight the importance of new data sources and evidence-based engagement by policymakers.Over the last 40 years, the secular increase in longevity in the US has slowed relative to peer countries, a phenomenon capped in more recent years by an unprecedented decline in US life expectancy . These bDuring this time, economic outcomes in the same population subgroups have worsened as well. Since the late 1970s, growth in wages and incomes stagnated for most US residents . The abiWe argue that worsening economic outcomes among low-income and less-educated working-age adults may be a key driver of adverse population health trends in the US. We begin by describing trends in economic outcomes since the late 1970s and their underlying drivers. We then discuss the myriad ways in which these trends have affected population health, focusing primarily on working-age adults. We review existing and new interventions that may jointly improve economic outcomes and population health . We also highlight the importance of new and more accessible data to better inform policy and the role of political processes in realizing the full potential of evidence-based policymaking.One of the most striking changes in the US economy has been the divergence, since 1980, in economic outcomes by level of education and relative position in the income distribution. Through the late 1970s, inflation-adjusted  wages grew at similar rates for US residents across the income distribution. Thereafter, however, real incomes stagnated for individuals in the bottom 50%, even as they continued to increase for those in the top 10% . This diSeveral forces may have contributed to these trends. The disappearance of employment opportunities that had previously provided to individuals without a college education a credible path to the middle class—e.g., manufacturing jobs—has played an outsized role ,12,13. TIn addition to these market forces, shifts in public policy have also contributed to rising income inequality and falling social mobility in the US. Starting in the late 1970s, federal minimum wage increases have failed to keep up with inflation, and the inflation-adjusted minimum wage today is lower than it was in 1980. Recent studies have demonstrated the critical importance of minimum wage increases in sustaining incomes of low-wage workers, with attendant consequences for the shape of the overall income distribution ,19. SecoFourth, further entrenchment of structural racism has contributed to economic inequality by eroding economic outcomes among historically minoritized populations . For exaPolicy-driven erosion of the social safety net has compounded the adverse socioeconomic consequences of the changing economy on low-income and less-educated individuals . For exaWorsening economic outcomes for less-educated and low-income individuals can influence health through several channels ,40,41 F. FallingThe diverging fortunes between the “haves” and “have-nots” may change or constrain the way individuals think and plan for the future, leading to underinvestment in behaviors that may improve health and economic outcomes. For example, worsening economic outcomes can diminish one’s expectations for a better future, which can undermine individuals’ motivations to engage in health-promoting behaviors –50. In aDescriptive evidence on trends in life expectancy and mortality by socioeconomic status implicate changing economic conditions as a key driver of worsening population health in the US. Most starkly, worsening health outcomes in the past 40 years have been concentrated among working-age adults in the bottom of the income distribution and among those with relatively low levels of formal education . These are the very groups that have increasingly fallen behind in economic outcomes since the 1980s . For exaDifferential experiences across geographic areas are also consistent with economic factors playing a critical role in driving population health outcomes. This phenomenon can be revealed by even cursory inspection: for example, age-adjusted mortality rates in counties in different income and education deciles tracked closely with each other until the 1980s, after which the richest and more highly educated counties experienced much larger declines in death rates . Excess Studies examining the health consequences of specific economic insecurities among low-income and less-educated adults provide still more supporting evidence. Many of these studies have the important advantage of leveraging “natural experiments,” which are research designs that use sudden unanticipated events or shifts in policy to account for unmeasured factors that may bias estimates of the associations between economic outcomes and health , allowinA growing literature has also investigated the mechanisms that plausibly may link worsening economic outcomes to health. Demonstrating that key hypothesized mediators influence health provides even greater confidence that economic changes in the past 40 years have adversely affected health. Several studies have examined the health consequences of increasing precarity: food, housing, and economic insecurity, for example, have been robustly linked to a range of health outcomes, including mortality –80. A nuOur review of the evidence thus far has focused primarily on short- and medium-run impacts of economic shifts on the working-age individuals who are directly affected. However, worsening economic conditions may also negatively affect the health of those exposed to these changes early in life, prior to entering the labor market. Young adults entering the US job market during economic downturns are more likely to earn lower lifetime wages, engage in health risk behaviors such as smoking and excessive alcohol use, and, by their late 30s, have higher mortality risk, compared with their counterparts entering the labor market during better times ,84. SimiExisting research highlights a number of social and economic policies that may jointly improve economic and health outcomes. Intervening in infancy and childhood appears to be most effective in achieving these objectives, and consequently, such interventions tend to have the highest social returns on investment . The colEvidence from randomized and natural experiments demonstrates that investments made before the age of 5 can lead to improved economic and health outcomes in adulthood –94. PoliThere is comparatively less evidence to inform the deployment of policy interventions aimed at working-age adults to address worsening economic outcomes and population health. Whatever evidence exists, however, suggests that the right approach will likely involve bundling multiple strategies. There is emerging evidence that minimum wage increases and the Earned Income Tax Credit result in fewer deaths from suicide among individuals with lower levels of education –107. PolFor historically marginalized population groups such as Black Americans, improving health outcomes may additionally require supplementing universal economic and social policies with specific interventions to reduce long-standing, historically patterned barriers to social mobility. Longevity gaps between Black and White Americans persist and closely track with similar gaps in economic opportunity . The posIt will also be important to invest in social policies that break the link between worsening economic outcomes and worsening health. A growing body of research demonstrates that expansions of state Medicaid programs have both reduced debt-driven economy insecurity among low-income adults and improved health outcomes –121. BolThe size and scope of population health declines among low-income and less-educated working-age adults also call for identifying, developing, and testing new intervention approaches. The threat of continued job displacement from automation has sparked interest in Universal Basic Income (UBI) cash-transfer programs. Early evidence from universal income programs (including ongoing programs in Alaska and the Eastern Cherokee nation) suggest positive impacts on health and educational attainment (without any evidence of the program inducing exit from the labor force) . These fInnovations in public health and healthcare systems—specifically those that help buffer population health against threats from adverse economic shocks—should also be explored. For example, there is growing interest in addressing social determinants of health among state Medicaid programs and hospital systems. Specific pilot programs  have been shown to be effective in this regard –137, thoA detailed understanding of how the changing economy has affected population health has been made possible by newly available large administrative databases and innovations in statistical techniques. New insights on the changing relationship between income and health have come from combining data from tax databases with mortality data from Social Security death records . EvidencAlthough many of these novel datasets have been gathered or built by individual research groups, there is an increasing trend toward data sharing, further catalyzing research on the economic determinants of health. For example, researchers releasing deidentified, aggregate versions of their tax-record data have enabled other scholars to identify new insights on the relationship between economic opportunity and health ,144,145.Despite these encouraging trends, researchers and policymakers still face significant challenges obtaining the data needed to best inform policymaking. Large, annual sample surveys primarily collect detailed information on health outcomes or economic outcomes, but not both, making it difficult to study the nuances of how economic trends shape various dimensions of health in different populations. Linking large-scale, individual-level federal economic datasets to health datasets such as death records or medical claims remains a costly, time-consuming process for most researchers. As a result, researchers studying the health consequences of adverse economic trends have had to rely on aggregate data, which are prone to their own biases  and make it difficult to study heterogeneity across population groups. Moreover, most of these data are released from 1–2 years after collection, making it difficult to provide real-time information on key questions of interest.The increasing availability of proprietary private data sources and high-frequency, crowd-sourced data has helped address some, but unfortunately not all, of these constraints. Consequently, new policies to increase access to existing large-scale administrative and survey data are needed to maximize our understanding of what policies are most effective in addressing the twin challenges of worsening economic outcomes and population health in the US. Steps to reduce administrative and financial barriers to accessing and linking state and federal data resources will be critical. In this respect, state and federal agencies could follow the lead of the National Institutes of Health (NIH), who recently made data linkages with the death registration system free for NIH-supported researchers, streamlined the process to apply for these data and increased the frequency at which these data were updated. Insights from the development and management of wide-ranging linked registries, such as those in Sweden or Denmark, could also provide a useful model. Agencies can also consider making some currently restricted-access data  public by adopting newly developed techniques to add noise to these measures in a manner that reduces loss of privacy while maintaining statistical fidelity .The full potential of data-driven policymaking to improve economic and health outcomes cannot be realized without buy-in from policymakers. There needs to be consensus and political will among policymakers around investing in data and acting on evidence. Translating evidence into policy will require a detailed understanding of the political considerations, policymaker values, and decision-making processes that influence policy adoption ,148. EviIn addition, it will be important to elucidate the complex feedback loops between health, economic outcomes, and voting patterns that will ultimately dictate which policies are adopted . ExposurPopulation health in the US stands at a critical juncture. Growing evidence suggests that fading economic opportunities and rising economic insecurity have played an important role in the deteriorating health outcomes and rising mortality rates experienced by working-age individuals. These trends may be further exacerbated by the SARS-CoV-2 pandemic, whose adverse economic consequences—and wide-ranging direct and indirect negative effects on a variety of health outcomes—are expected to most heavily affect low-income individuals. Despite these troubling trends, there are reasons for optimism. New data have enabled researchers and policymakers to better delineate emerging population health challenges and identify policies that can reduce growing health and economic inequality. Further investments in new data and ensuring better access to, and linkages between, existing databases will be necessary to fully realize the potential of evidence-based social and economic policy. However, data alone will not be sufficient. Policymaker consensus around the value of data—and the political will to act on it—will be critical for translating evidence into improvements in population health.


Cardiomyocytes, the cells generating contractile force in the heart, are connected to each other through a highly specialised structure, the intercalated disc (ID), which ensures force transmission and transduction between neighbouring cells and allows the myocardium to function in synchrony. In addition, cardiomyocytes possess an intrinsic ability to sense mechanical changes and to regulate their own contractile output accordingly. To achieve this, some of the components responsible for force transmission have evolved to sense changes in tension and to trigger a biochemical response that results in molecular and cellular changes in cardiomyocytes. This becomes of particular importance in cardiomyopathies, where the heart is exposed to increased mechanical load and needs to adapt to sustain its contractile function. In this review, we will discuss key mechanosensing elements present at the intercalated disc and provide an overview of the signalling molecules involved in mediating the responses to changes in mechanical force. Mechanical stimuli play a key role in both heart morphogenesis and in the mature heart. During heart development, mechanical forces orchestrate the molecular and cellular changes that transform the linear tubular heart into a multichambered machine with four valves  are large, modular, membrane spanning proteins of adherens junctions, located at ideal sites to sense and transduce changes in tension: on the extracellular side, they mediate cell-cell adhesion with another cadherin molecule on a neighbouring cell, whereas on the intracellular side, they are anchored to actin filaments via a complex of catenins   is required for vinculin recruitment at cell-cell junctions, and once recruited, vinculin can stabilise the actomyosin contractile machinery. A paradox of this feedback system is the ability of myosin II to sever F-actin filaments, thus restricting actin assembly  as a vinculin-binding partner in contracting cells , a striated muscle-specific protein which was proposed to be part of the mechanical link between the intercalated discs and myofibrils  whose extracellular domain interacts with another desmosomal cadherin molecule on a neighbouring cell, while the intracellular domain links to intermediate filaments via the cytosolic armadillo proteins Pg (γ-catenin) and Pkp and the plaque protein desmoplakin (DP) Fig. . DesmosoRecent studies have shown that desmosomes are not rigid but can act as mechanosensory structures  are found at both the intercalated disc and costameres, yet their function in mechanosensing at the intercalated disc remains unclear. It will be important to study these proteins at the cellular level, in confluent cardiomyocytes, which allow cell-cell adhesions to be formed, to carefully dissect their precise localisation, their binding partners and their dynamics at the intercalated disc in response to mechanical force.2+-selective channel found at the intercalated disc in the murine heart  channels responds to numerous physical and chemical stimuli via a conformational change to allow cation influx into the cell   which binds to the IGF1 receptor (IGF1R) to activate the PI3Kα pathway McMullen . NeonataRhoA and its associated kinase (ROCK) signalling are essential in actomyosin machinery assembly, with ROCK phosphorylating and activating myosin regulatory chains but also formins which ensure unbranched F-actin nucleation, elongation and/or bundling (Arnold et al. RhoA/ROCK signalling at the intercalated disc appears to play a role in AC (Vimalanathan et al. The intercalated disc contains a complex machinery of structural and signalling molecules which work together to sense, transmit and transduce changes in force to ensure the heart maintains its function in response to increased mechanical stress. It is a fine-tuned machinery and maintained stoichiometry of its composition appears to be crucial for its integrity and heart function. Work carried out in recent years has shown that mechanosensors established by investigating cell-cell contacts in epithelial cells also play a role in mechanotransduction at the intercalated disc, but much still remains to be unveiled. Several questions, such as the precise role of integrin complexes in mechanosensing at the intercalated disc, how desmosomes sense various mechanical loads and whether there are other actin regulatory factors affecting actin structures at this specialised site, will hopefully be answered in the next decade. A clearer understanding of intercalated disc mechanosensing and signalling will allow the developing of pharmaceutical agents that help the failing heart to cope better with the increased mechanical load experienced in disease.