Internally generated imagery and externally triggered perception rely on overlapping sensory processes. This overlap poses a challenge for perceptual reality monitoring: determining whether sensory signals reflect reality or imagination. In this study, we used psychophysics to investigate how imagery and perception interact to determine visual experience. Participants were instructed to detect oriented gratings that gradually appeared in noise while simultaneously either imagining the same grating, a grating perpendicular to the to-be-detected grating, or nothing. We found that, compared to both incongruent imagery and no imagery, congruent imagery caused a leftward shift of the psychometric function relating stimulus contrast to perceptual threshold. We discuss how this effect can best be explained by a model in which imagery adds sensory signal to the perceptual input, thereby increasing the visibility of perceived stimuli. These results suggest that, in contrast to changes in sensory signals caused by self-generated movement, the brain does not discount the influence of self-generated sensory signals on perception. Visual signals can be triggered externally, by the appearance of a stimulus in the outside world , or internally, via top-down processes such as mental imagery. Neuroimaging evidence has shown that neural representations of internally and externally generated signals are highly similar . This ovdecreased the likelihood that participants detected external stimuli, which was interpreted as showing that the external input was erroneously being attributed to imagery , and data were collected on an institutional server managed by the JATOS tool (SD = 11.5).Based on power calculations of a previous experiment with the same paradigm , we requTOS tool . All parnot vivid at all) to 5 , similar to the scale used in the VVIQ2.The experimental paradigm is depicted in For the main task, participants were instructed that a grating of one orientation would be presented on some of the trials and that in some cases, the grating would be quite hard to see. In order to avoid visual priming, no trial-wise cues were delivered, and instead the different conditions were implemented in a block-wise fashion such that during the entire block, participants detected one specific orientation and imagined one specific orientation. At the onset of each experimental block, the participant was instructed which grating orientation would be shown and what they had to imagine during the block A as follAt the end of the experiment, after filling out their age, we also asked participants whether they thought imagining the gratings had altered their responses on the detection task. The replies varied, with comments such as “I think imagining either left or right tilted gratings helped me see the ones that were actually there” and “I made some mistakes in the answers because I got confused between the pattern that I imagined and the one that was displayed.”o or 135o masked with an annulus and embedded in white noise  and consisted of a sinusoidal grating at an orientation of 45te noise A. The vite noise , that iste noise B. The exFor each orientation separately, 50 stimulus images were generated and distributed equally in log space across the seven visibility levels (from 0% to 14%). For the pure noise trials, an additional 20 images of pure white noise were generated. For the trials with a visibility level above zero, 20 stimulus images ranging from zero visibility to that specific visibility level were presented over the course of 2 s, giving the impression that the stimulus was gradually appearing in the noise. This ramping up was done to mimic the gradual nature of mental image generation . During presentP is the probability of reporting stimulus presence;  μ is the mean of the normal distribution, reflecting the horizontal offset of the psychometric curve/how much signal is needed to achieve 50% presence responses; σ is the standard deviation, reflecting the slope or sensitivity of presence responses to increases in signal; and g is the guess rate, reflecting the vertical offset at the zero point, or how likely presence responses are in the complete absence of signal. We did not include a lapse rate  = 7.34, p = 0.002, ηp2 = 0.273). Post hoc comparisons revealed that the amount of signal needed to achieve 50% presence responses was significantly lower during congruent imagery  than during no imagery  = 2.97, p = 0.005) or during incongruent imagery  = 3.63, p = 0.0007). Furthermore, the mean was also significantly higher during incongruent imagery compared to no imagery (t(40) = 2.12, p = 0.038). In contrast, there was no significant effect of imagery condition on either the slope  = 1.325, p = 0.277) or the guess rate  = 0.169, p = 0.845). We further quantified the evidence for the presence or absence of a condition difference using Bayesian statistics in JASP (BF = 109.85), moderate evidence for the absence of an effect on slope (BF = 0.29), and strong evidence for the absence of an effect on guess rate (BF = 0.08).The curve fits and proportion of presence responses per visibility level for the three conditions are shown in  in JASP . In keepIn this study, we set out to characterize how internally and externally generated visual signals interact when stimuli are simultaneously imagined and perceived. To this end, we estimated psychometric functions while participants were simultaneously detecting and imagining oriented gratings. We found that congruent imagery was associated with a leftward shift of the psychometric function compared to both no imagery and incongruent imagery. In contrast, imagery did not have an effect on either the slope or the guess rate of the psychometric function. These results suggest that imagery adds sensory evidence to perceptual signals, thereby increasing the visibility of perceived stimuli.Imagery did not influence the guess rate of the psychometric function. This makes it unlikely that imagery led to a response bias toward reporting stimulus presence, for instance, due to demand characteristics. Such a bias in the context of our experiment might have caused participants to blindly respond “yes” more often during congruent imagery, even in the absence of any changes in sensory processing . HoweverWhile the absence of an effect on guess rate rules out a simple response bias, a change in mean could be caused by effects at different levels of sensory processing. The effect could be due to either a change in low-level sensory representations and/or by a change in the decision variable in more high-level evidence accumulation circuits . Previousuppresses activity of incongruent populations—thereby “sharpening” the neural representation in line with imagination (The precise implementation of how exactly imagery adds to the sensory signal remains unclear. One intriguing possibility is that imagery, like expectation, not only enhances activity of neural populations congruent with the imagined stimulus  but alsogination . This idgination . Future The consistent increase in perceptual presence responses for congruent imagery reported here and elsewhere  suggestsany change in external visual input or whether such signals were generated by imagination—that is, in the current experiment, answering the question, “Was there a grating presented on the screen?” Answering yes to this question already implies that the sensory signal has an external source and was not imagined. In other words, attributing sensory changes during action to self can be associated with an increase in perceptual presence responses, whereas attributing sensory changes during imagery to self should be associated with a decrease in perceptual presence responses. Such decreases have been observed in the past (However, there is an important difference between reality monitoring of sensory signals during action and reality monitoring during imagery. During action, the sensory signals are always assumed to reflect real changes in external input—either caused by our own movement or by changes in the outside world. In other words, reality monitoring during action is not about whether there is a change in the external visual input of our hand but about whether that change is caused by us moving our hand or by some outside source. In contrast, reality monitoring during imagery involves a lower-level decision about whether there was the past , but recthe past . A failuthe past . More reTaken together, our results suggest that congruent imagery increases the probability of reporting perceptual presence by adding sensory evidence in favor of the imagined stimulus. These findings indicate that the brain does not discount the impact of self-generated sensory signals generated during imagery on perception, instead treating it as additional evidence for the presence of external input.


Given the rapid recent trend of urbanization, a better understanding of how urban infrastructure mediates socioeconomic interactions and economic systems is of vital importance. While the accessibility of location-enabled devices as well as large-scale datasets of human activities, has fueled significant advances in our understanding, there is little agreement on the linkage between socioeconomic status and its influence on movement patterns, in particular, the role of inequality. Here, we analyze a heavily aggregated and anonymized summary of global mobility and investigate the relationships between socioeconomic status and mobility across a hundred cities in the US and Brazil. We uncover two types of relationships, finding either a clear connection or little-to-no interdependencies. The former tend to be characterized by low levels of public transportation usage, inequitable access to basic amenities and services, and segregated clusters of communities in terms of income, with the latter class showing the opposite trends. Our findings provide useful lessons in designing urban habitats that serve the larger interests of all inhabitants irrespective of their economic status. Added to this, rising economic inequality jeopardizes the health and livelihoods of many urban residents. Indeed, the percentage of Americans living in middle-income neighborhoods has decreased from 65 to 42% in the last 40 years, while the inhabitants of neighborhoods at the lower and higher ends of the income spectrum have grown5. Much of the urban growth has been driven by the less wealthy who have progressively moved from the rural areas to cities6. While the rise in economic inequality is a global phenomenon, different patterns are observed between countries and cultures. For instance, while low-income households are typically located in the outskirts in Paris, they live downtown in Detroit7. Such patterns of economic segregation and inequalities are connected as well to racial segregation, urban decay8 and gentrification9. In fact, over the course of history, many policies and resulting infrastructure changes were put in place in ways that hurt minority communities and already vulnerable portions of cities. This has been thoroughly documented in10 for the case of New York and similar instances have been occurring around the world. Understanding the factors behind this and developing public policy to alleviate these trends is therefore crucial to enhance social mobility and economic progress, as well as positively impact the health of citizens14.The recent trend of rapid global urbanization15. These cities follow a distinct pattern of urbanization, transitioning from strong industrial centers to a decentralized manufacturing pattern16. Additionally, the layout of a city’s natural resources and amenities can bias a population to concentrate in locations other than the expected city center, although urban layout is by no means static. Development induces change in residential patterns change, even more so, during periods of gentrification, causing shifts in the income profile of neighborhoods over time17. Varying welfare policies and housing market conditions affects cities differently across locations; the decline of neighborhoods into poorly maintained areas is more strongly felt in American cites than European ones8.To understand the unique sociodemographic and economic context of a city, one needs to take into account a number of factors. For instance, some cities in developing countries concentrate industry in city centers, leading to unfavorable urban conditions such as pollution22. Underlying these intra-city mechanics are the people, and their mobility patterns23. Indeed, the increasing accessibility of location-enabled devices and large-scale datasets of human activities, such as credit-card purchases, taxi rides and mobile phone usage are fueling significant advances in our understanding of human mobility behavior32. Yet, the connection of these observed mobility patterns with socioeconomic status is surprisingly unclear and in many cases contradictory.Residential inequality is only a partial view of the whole picture, given that cities are a product of several interconnected systems. City infrastructure and dynamics are significantly connected to their underlying social systems, in particular influencing development and productivity indicators33, a similar study in Louisville, KY, revealed that low-income residents tended to travel further on average than those in affluent ones34. On the other hand, analysis of cell phone data from an emerging Latin American economy revealed that wealthier citizens traveled to more locations and longer distances35. An analysis of mobility in Bogotá, Colombia found the most mobile population to be the upper-middle class instead of the wealthiest36. Employment status played a role in movement patterns in both Riyadh and Spain, where the unemployed tended to travel less and spend more time at home38. Two studies in French municipalities found a connection between the diversity of location visits with income, but no connection to the distance traveled40.For instance, while mobility patterns across different socioeconomic classes exhibit very similar characteristics in Boston and SingaporeThe variance in the results, indicate that the observed diversity in trends are the result of a complex interplay between urban infrastructure and socioeconomic processes. However, these were conducted in a relatively small set of cities, with different underlying datasets and methodologies. Greater clarity and insight may be uncovered by conducting the studies at scale, globally, and with a uniform methodology. To that effect, here, we explore the multiple facets connecting human travel behavior in a city to its socioeconomic landscape, through a country-wide analysis encompassing one hundred cities in the United States and Brazil. The two countries were selected due to their similarity in total population and the number of large cities, with one being a developed economy, while the other an emerging economy with markedly different socioeconomic characteristics. We find differences within and between the two countries, with two distinct classes of cities; one where there is a strong connection between the socioeconomic status of the residents and their movement patterns, and another class where there appears little-to-no connection. The latter class of cities are characterized by wider use of public transportation, equitable access to amenities and services as well as greater mixing among neighborhoods in terms of income profiles. The former class of cities show the opposite trends. We conclude with a discussion of the implications of our findings, including possible policy directions as it relates to urban planning.sub-areas (SAs).The Google Aggregated Mobility Research Dataset contains anonymized mobility flows aggregated over 300 million users who have turned on the Location History setting, which is off by default. This is similar to the data used to show how busy certain types of places are in Google Maps, helping identify, for instance, when a local business tends to be the most crowded. The dataset aggregates flows of people between regions, specifically ZIP Code Tabulation Areas (ZCTAs) for the United States, and the comparable census Weighting Area (WA) for Brazil, both of which we generically refer to as 4. To provide strong privacy guarantees, all trips were anonymized and aggregated using a differentially private mechanism to aggregate the flows41. This research is done on the resulting heavily aggregated and differentially private data. No individual user data was ever manually inspected, only heavily aggregated flows of large populations were handled. As a result, the data is represented as a flow matrix i to j for the year 2016. For the purposes of our analysis we consider the 50 largest cities in the United States and analogously in Brazil ranked according to their population. In Fig. To produce this dataset, machine learning is applied to logs data to automatically segment it into semantic trips42, and analogously the 2010 decennial census for Brazil43. Among the metrics collected were age, race, level of education, population, median income, sex composition, among others. A representative sample of the data, aggregated at the level of ZCTA’s is shown for Rochester, NY in Supplementary Table S3, and at the level of WA’s for Campinas in Supplementary Table To represent socioeconomic status we collected data from the most recent 2016 5-year American Community Survey (ACS) for the United Stateshigh and low income sub-areas as the top and bottom median income quintiles within their city. This metric is useful for simply categorizing the income of different sub-areas in a city. Our second metric, In Supplementary Figs. 44, that contains geo-referenced information for a broad array of urban amenities, including schools, banks, libraries, groceries and universities, to name a few. The OSM data contains 691 different types of urban facilities belonging to eight main classes: healthcare , sustenance , financial , education , art-culture , entertainment , transportation (bicycle parking and bus station) and others .Mobility patterns of urban residents are not only influenced by considerations of cost or income constraints, but also by the number and diversity of amenities and services accessible to them. To collect this information, we query the OpenStreetMap (OSM) databaseFor the purposes of our analysis, we consider basic amenities related to food, healthcare, education and finance. A representative list of such amenities is shown for four different cities in the United States in Supplementary Table i it can be computed asi to a different area j and i, computed asi. The city-level values are then computed by summing over all locations i within the city. The measure serves as a proxy for trip frequency which is a useful complement to trip distance, as it represents an activity rate. Activity patterns, which are connected to employment status38, combined with average distance traveled, indicate the distance and frequency of travel for residents in a given location. These factors are potentially influenced by the socioeconomic resources of the residents as well as their proximity to opportunities, and enables targeted ways of understanding the influence of city infrastructure and services on its residents.We characterize the mobility patterns of an area based on two representative quantities. The first metric is the average travel distance, or weighted average out-flow length, which is intended to capture the mobility-related costs that are directly proportional to travel distances. For each location https://www.pewresearch.org/global/2016/02/22/smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies/).We begin with a country-wide analysis of the mobility patterns of the lower and upper 20% of SA’s by median income. The choice of income range enables a first look at the trends for residents at the opposite end of the socioeconomic spectrum, that is those with the most and least potential constraints on their traveling behavior. In Fig. n income intervals, we construct for each urban area, n income distribution vectors m positions each m denotes the number of SAs in that city. Here, n lives in SA m. We compute the Spearman rank correlation coefficient between each m. For the results reported here, Given the observed country-level signal between the mobility metrics at the opposite ends of the income scale, we next conduct a more granular analysis across all census-determined income breaks In Fig. ist Fig. A and a preq Fig. B, indicaThe Brazilian cities, on the other hand display markedly distinct trends. While there are a few cities with relatively little correlations such as Caxias do Sul, Campinas and Blumenau, the majority of cities display high positive correlations across metrics and income ranges, with the strongest signal being in the middle-income range Fig. C,D. Wher45, finding a clear partition of the cities in the United States into two different groups, color-coded in teal and orange as shown in Fig. The results from Fig. In Fig. 4, that those cities that are in the orange cluster tend to be more hierarchical and centralized in terms of mobility-flows as compared to those in the teal cluster that are sprawled and decentralized. Hierarchical cities have higher levels of infrastructure for public transit, and the observed higher usage of public transit is not just a behavioral feature but also reflects wider availability.We next investigate the factors behind this difference in correlations. A way to mitigate the disadvantages due to income is having efficient and cheap public transportation systems. In Fig. As mentioned earlier, another feature that might influence mobility patterns is the availability of amenities and services. Correspondingly, we check the typical distance residents of a particular location have to travel before they come across a number of basic amenities . For ease of display, instead of using all eight income breaks, we split the income groups into five categories  and plot the fraction of amenities accessible up to 10km from the resident location .Taken together our results shed greater clarity on the connection between socioeconomic features and movement patterns as compared to previous studies, given the scale of our investigation. A study of a hundred cities in two large countries comparable in size and population but differing in levels of economic development, revealed that cities can be broadly classified into two categories; those where resident’s movements are influenced by their income profile (with varying degrees of influence), and those where mobility is largely independent of their socioeconomic condition. This classification of course applies in an averaged sense, given the differences in cities both between and within the countries.The two categories find their clearest manifestation in the United States, with a roughly even split between the 50 cities, that was uncovered with a clustering approach, with cities in the income-correlated cluster labeled teal and those in the weakly-correlated cluster, orange. The wider availability of public transportation in the orange cluster appears to be a feature that mitigates the correlation between income and movement, perhaps playing the role of an equalizing feature for those in the lower income brackets. Another key aspect is the availability and diversity of basic services. While lower-income residents in the teal cluster are on average closer to basic amenities, this appears to be a function of them living in the city-center, with the higher-income groups concentrated in suburbs. When it comes to the diversity of services available, there is a clear advantage to those in the higher-income bracket. These cities are also segregated by income, whereby high-income and low-income residents tend to be concentrated in specific areas. This is also manifested in the parts of the cities that they visit, with relatively little mixing between the income groups. Indeed, given the connection with income and race Figs. , S2 thisIn Brazil, we also find two categories, however unlike in the United States, movement in both clusters is correlated with socioeconomic features, with the difference being one cluster (blue) displays even stronger correlations then the other (yellow). While public transportation usage is comparable across cities, the accessibility to services in terms of proximity and quality is strongly skewed towards the highest income brackets in the blue cluster, although the income advantage is also found to a lesser extent in the yellow cluster. Additionally cities in the blue cluster have a more segregated residential profile than those in the yellow one. Both clusters, however display, a sharp divide between rich and poor in terms of which parts of the city is visited, with the rich being concentrated in the city-center and the poor primarily moving in the periphery. In some sense, the Brazilian cities mirror the teal cluster from the United States, with stronger differences in mobility in terms of the socioeconomic characteristics.To validate our results, we also conducted our analysis on a different dataset; the commuting patterns extracted from the United States census. The qualitative results are essentially identical, with some differences in the clustering of the cities, stemming from the fact that the census does not accurately capture the frequency of travel. This resulted in a few cities changing clusters, but by and large the differences in terms of transportation usage, accessibility of services and residential segregation were preserved, attesting to the robustness of our analysis. A similar analysis could not be conducted for Brazil given that such data is not available in that country’s census. This points to another strength of this new approach: its applicability globally by leveraging aggregate signals such as mobility that are inexpensive to compute and available in a timely fashion. Methods relying on traditional census data do not generalize to many regions where census is unavailable, unreliable, or considerably delayed in time.48, although the extent of its efficacy may vary from region to region, given the observed differences between the United States and Brazil. Certainly more efforts must be made in terms of improving the quality of services in low-income areas. Given that this disparity might arise from the residential patterns seen in both countries, inclusive zoning and incentives to foster mixed-income neighborhoods may well be an effective policy50. Indeed, such initiatives have been tried in cities such as New York, Boston and Chicago, which all incidentally lie in the orange cluster.To the extent that it is undesirable to have cities with residents whose ability to navigate and access resources is dependent on their socioeconomic status, public policy measures to mitigate this phenomenon are the need of the hour. While it is difficult to disentangle the causal mechanisms behind the observed disparities, investment in affordable and robust public transportation may enhance equality of accessNevertheless, much remains to be uncovered, in particular whether the reported trends here exist in countries in different regions and varying degrees of economic development. Effective strategies and measures will also vary from region to region and more tailored studies would be required to identify the contextual causes of different socio-mobility profiles. We anticipate many more studies along this direction in the near future.Supplementary Information.


We analyzed the impact of three major socio-economic factors  on PM2.5 concentrations for 254 cities from six developed countries. We used the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model with three separate data sets covering the period of 2001 to 2013. Each data set of 254 cities were further categorized into five subgroups of cities ranked by variable levels of income, density, and population. The results from the multivariate panel regression revealed a wide variation of coefficients. The most consistent results came from the six income coefficients, all of which met the statistical test of significance. All income coefficients except one carried negative signs, supporting the applicability of the environmental Kuznet curve. In contrast, the five density coefficients produced statistically significant positive signs, supporting the results from previous studies. However, we discovered an interesting U-shaped distribution of density coefficients across the six subgroups of cities, which may be unique to developed countries with urban pollution. The results from the population coefficients were not conclusive, which is similar to the results of previous studies. Implications from the results of this study for urban and national policy makers are discussed.Despite numerous studies on multiple socio-economic factors influencing urban PM According to a recent study involving 381 large cities with populations of more than 0.75 million people in China, India, the U.S., Europe, Latin America, and Africa [2.5 concentrations from 2000 to 2006 in 23.9% of these cities was higher than the World Health Organization’s (WHO) interim Target 1 of less than 35 micrograms per cubic meter (35 μg/m3). In addition, only 18.0% of these large cities were within the recommended WHO target of less than 10 μg/m3. Large cities in Asia, especially China and India, had the worst record, with 48.7% of these cities recording PM2.5 concentrations higher than 35 μg/m3 and only 1.7% with PM2.5 concentrations of less than 10 μg/m3. In contrast, large cities in Latin America had the best air quality, with 64.4% of them within the 10 μg/m3 guideline.Heavy fine particulate (PM2.5 hazard (35 μg/m3 or more) in 2010. These people resided in 154 cities, which represented 78% of all large cities with a population of more than 1 million. To make matters worse, 278 million more people became exposed to PM2.5 hazards between 2000 and 2010 due to the high birth rate and high migration rate from rural areas to these cities.Translating urban pollution risk in terms of the number of people, it has been reported  that mor2.5 pollution in urban centers is needed to develop effective mitigating policies to control urban pollution. However, the unavailability of relevant city-wide technology-related data, such as the industrial structure, energy intensity, service structure, vehicle usage, as well as income per capita has been a barrier to the productive flow of research. Fortunately, however, the unavailability of city-wide data has become somewhat more manageable in recent years, increasing the number of necessary studies.In short, the air pollution risk appears to be far more serious in urban settings in large cities in several countries in Asia, particularly in China, India, and Pakistan. Therefore, a high priority for research on socio-economic influencing factors of PM2.5 concentrations for 73 Chinese cities. The results showed that secondary industries, including manufacturing, construction, fast moving consumer goods, and other industries, and the vehicle population, in that order, had greater impacts on PM2.5 concentrations in these cities. Wu et al. [2.5 data from the same 73 cities in 2013 and 2014 and determined that PM2.5 significantly correlated with the proportion of industrial activity, the number of vehicles, and household gas consumption in these cities.A large majority of these studies have focused exclusively on urban pollution in China ,10,11,122.5, and GDP per capita had a moderate impact on PM2.5. Cheng et al. [2.5 concentrations for 285 Chinese cities from 2001 to 2012. Their results indicated that population density, income, and traffic intensity had a significant impact on PM2.5 concentrations. In addition, secondary industries and central heating significantly aggravated urban air pollution. However, foreign direct investment was not a significant factor.Expanding the number of cities to 338 Chinese cities from 2014 to 2017, Wang et al.  determin2.5 concentrations in cities in developed countries have been published in recent years [2.5 concentrations from 2001 to 2013.In contrast to the many studies on urban pollution, only a few socio-economic studies on PMnt years ,14. The 2.5 pollution is presented in the next section, followed by a section explaining the STIRPAT model and data sources. An analysis of the results is presented in the fourth section. Finally, the conclusion, implications, and limitations of the study are presented in the fifth section.After this introduction, the paper has four sections. A brief literature review on selected socio-economic studies on PM2.5 concentration in urban centers in recent years have concentrated on China [2.5 is the main component of haze and fog for large cities in China, so investigating the relationship between socio-economic factors related to PM2.5 pollution is very important to Chinese policy makers. Another reason is that city-level data for PM2.5 pollutants and related socio-economic factors in many other developing countries are mostly unavailable. In terms of developed countries, PM2.5 is not as critical of an environmental pollution issue for cities in developed countries [2.5 concentrations for cities in developed countries have been published recently on cities in the U.S. [The large majority of socio-economic studies on PMon China ,10,12,15the U.S.  and Germthe U.S. . A groupthe U.S. ,19,20,21the U.S. ,23.2.5 concentrations in China for a closer examination [We selected the five most representative papers on the socio-economic analysis of city-level PMmination ,8,9,15. 2.5 concentrations, depending on the income level of cities. All five articles found a statistically significant inverse U-shaped or inverted N-shaped environmental Kuznet curve (EKC). For example, Cheng et al. [2.5 pollution. Similarly, Liu et al. [The most consistent finding is related to the increasing or decreasing impact of city GDP per capita to PMg et al.  estimateu et al.  conclude2.5 pollution. For 108 cities in the eastern region, there is an inverted N-shaped curve with a projected inflection point of $9186. As many as 64% (69) of the cities have reached their inflection point, and another 18% (19) cities are expected to reach their inflection point within the next five years. The remaining 47 cities in the western region do not follow the EKC and show a linear positive relationship between income per capita and PM2.5 pollution.Wu et al.  also est3 in PM2.5.Wang and Fang’s  study on2.5 concentrations.Nearly the same findings can be found for industrial structure, measured by the proportion of value added by secondary industry to GDP as well as traffic intensity measured by the proportion of the number of civilian vehicles to the total length of urban roads. In short, four of the five articles  confirm2.5 pollution has had somewhat contradictory results in these five articles. The findings by Cheng et al. [2.5 concentrations derived from three separate panel regression models for 285 cities in China from 2001 to 2012 generated all six population density coefficients ranging from +0.06 to +0.029, which were statistically significant at the 1% level. In other words, a 1% increase in density increased PM2.5 concentrations from 0.029% to 0.06%, while the effects from other factors such as income, industrial structure, electricity intensity, traffic intensity, and several others held constant. The other two articles by Hao and Liu [The impact of population density on PMg et al. , Wu et ag et al. , and Zhog et al.  were stag et al.  showed t and Liu  showed p2.5 pollution. Similarly, Wu et al. [2.5 pollution. In contrast, Zhou et al. [2.5 pollution.Another factor, energy or electricity intensity, has also generated somewhat contradictory findings in the three articles. For example, Cheng et al.  found a u et al.  found a u et al.  found no2.5 pollution. However, other papers such as Wang et al. [2.5 concentrations and urban population, together with the size of the urban areas, the share of secondary industry, and population density. Han et al. [2.5 concentrations in Chinese cities.It is interesting to note that none of the five articles examined the impact of the population size of cities on PMg et al.  reportedn et al.  and Han n et al.  suggeste2.5 pollution in developed countries are less clear as they vary from those reported on city level PM2.5 concentrations in China. A recent socio-economic analysis of PM2.5 pollution on cites in developed countries emphasized the role of population density over income or population size. For example, Carozzi and Roth [2.5 concentrations. Specifically, they found that doubling the density would increase the average PM2.5 pollution roughly 10% across 933 U.S. cities.The findings from socio-economic analyses of urban PMand Roth  found a 2.5 concentrations by a modest 0.08%. Using an authoritative survey, Ahlfeldt and Pietrostefani [2.5 pollution was positive in Chinese studies. Similarly, studies on U.S. and Germany cities also suggested that the effect of high population density was moderately positive.In another systematic study on 109 districts in Germany, which included 51 urban districts, Borck and Schrauth  found thostefani  cited boostefani  and Borcostefani , and recHowever, when population density is examined in the framework of urban spatial structure or urban form in relation to air pollution, studies of American and European cities have shown that low-density urban sprawl can lead to a significant deterioration of air quality ,19,28,292.5 pollution levels increase as population density increases. Another study for 249 European cities found that high density cities were more vulnerable to high levels of SO2 concentration [For example, Bereitsnaft and Debbaze  found thntration .In sum, it is likely that both very high- and very low-density cities may be subjected to higher levels of air pollution emissions. Thus, several recent studies have proposed adjusting population density upward by promoting monocentric urban structures for those low-density cities while adjusting population density downwards by promoting polycentric structures for the excessively high-density cities as possible remedies to reduce air pollution ,33,34.2.5 pollution, Anenberg et al. [2.5 concentrations across 82 global cities were negatively associated with city GDP per capita at a correlation coefficient of 0.64 at p < 0.0001. In other words, the negative impact from higher income cities in developed countries on PM2.5 pollution appears to be more pervasive compared to Chinese cities.As for the impact of income on PMg et al.  discover2.5 concentrations in 30 OECD countries from 1998 to 2015 using a threshold panel model. The result was that a 1% increase in GDP per capita decreased the PM2.5 concentrations from 0.3% to 0.4%, depending on the three income levels of countries. These findings supported the earlier studies by Wang and Fang [Another paper by Ouyang et al.  examinedand Fang  and Wangand Fang .2.5 pollution, Han et al. [2.5 pollution than those in U.S., European, and Latin American cities. For cities in India and Africa, they discovered a U-shaped trend for PM2.5 concentrations as urban population increased.As for the impact of population size of a city on PMn et al.  discoverMany scholars have used econometric models to analyze the influencing factors of energy usage and air pollution from a socio-economic perspective. Econometric models include both cross-section and panel models. The use of panel models has become popular as they can increase the sample size, reduce collinearity between variables, and control individual heterogeneity of samples to improve the reliability and validity of the estimates .The original IPAT model was refined later to become the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model, which enabled researchers to estimate the proportional change of the environmental impact per given proportional change in population, affluence, and technology.The STIRPAT model is defined asTo ease the task of estimating exponents, Equation (1) is converted into the log-log form of Equation (2) by taking the natural log of both sides.The natural log is helpful as it converts non-linear variables to linear ones, rendering the results interpretable as a percentage change. For example, b can be viewed as the population elasticity that measures the percentage change of the environmental impact resulting from a 1% change in population. The STIRPAT model has also been used to examine the impact of population, income, and/or technology in other areas such as the material footprint, human ecological footprint, and environmental efficiency of well-being ,37,38.2.5 pollution at the country level as well as the city level [Many scholars have also used the STIRPAT model to analyze the impact of socio-economic factors on PMty level ,44,45,46ty level . Accordity level , technolty level  extensivty level  also use2.For this study, population density is used to represent the technology factor in the STIRPAT model, together with the population size of a city as P and income per capita of a city as A, as shown in Equation (3):Y represents the dependent variable of PM2.5 concentrations; x represents the explaining variables of population size (P), income per capita (I), and population density (PD); θ1 and θ2 represent parameters to be estimated; q represents the threshold variable; γ represents the threshold quantity; and e represents the error term. Based on the variables selected in this study, the threshold model is expressed in Equations (6) and (7):In addition, we use threshold regression as a robust test to verify the results from the STIRPAT analysis. This study uses Hansen’s  thresholWe then combine Equations (6) and (7) using a dummy variable, which takes the value of one when the condition in parentheses is met, otherwise it becomes zero. This combined equation is used as the estimation equation of this research. The generalized threshold panel model has been used extensively in the fields of energy consumption, renewable energy development, and carbon emission on sustainable development ,55,56,572.5 in metropolitan areas from 2001 to 2013 (https://stats.oecd.org/index.aspx?DatasetCode=EXP_PM2_5_FUA accessed on 12 August 2020) for 706 cities in six countries: the U.S. (262 cities), Germany (109 cities), the U.K. (101 cities), France (82 cities), Japan (76 cities), and Spain (76 cities).We downloaded the data set indicating the exposure to PMhttp://stats.oecd.org accessed on 24 August 2020). After eliminating cities with missing data, we obtained the final sample size of 254 cities with a complete set of yearly data on PM2.5, population size, population density, and city GDP per capita. The final sample size of 254 cities included 59 cities in the U.S., 57 cities in Germany, 46 cities in Japan, 38 cities in France, 33 cities in the U.K., and 21 cities in Spain.We then downloaded the population size, metropolitan land area, and GDP from 2001 to 2013  test developed by Levin, Lin, and James , and theWe then tested for multicollinearity among the explanatory independent variables in all of the panel regression models using variance inflation factors (VIFs). The VIF values were all less than 10, as shown in In the regression of the SPIRPAT model, this research used the Prais–Winsten (PW) estimation method with panel-corrected standard error. The PW method uses a generalized least square framework that corrects for AR(1) autocorrelation within the panels and cross-sectional correlation and heteroscedasticity across panels .2.5 concentrations on the three separate groups of 254 cities by income, density, and population, and their respective five subgroups generated the following variable results. First, the full sample of 254 cities ranked by 2013 income per capita yielded a statistically significant −0.074. In other words, a 1% increase in income per capita generated a −0.074% reduction of PM2.5 concentrations, while the impact from the other factors of density and population size were held constant, as shown in STIRPAT multivariate panel regression of PM2.5 concentrations by 0.208% for the subgroup of 127 high-income cities. In contrast, the sample of the remaining bottom 127 low-income cities yielded a statistically significant coefficient of +0.157, while the other factors held constant. Specifically, a 1% increase in income increased PM2.5 concentrations by 0.157%, which indicated an income scale disadvantage for the bottom 127 low-income cities. These results suggest that there are effects from the EKC curve on cities with different income levels.When the subgroup of 254 cities was divided into the top 127 high-income cities, the income coefficient increased to −0.208, which was about 2.7 times larger than the income coefficient obtained from the 254 cities. A 1% increase in income per capita reduced PM2.5 pollution. Furthermore, all three income coefficients met the statistical test of significance.Furthermore, the contrasting results from the top 127 and the bottom 127 cities suggested the possibility of an even greater income scale advantage for cities with very high income per capita. Therefore, extended STIRPAT analysis of income coefficients for the samples of the top 15, top 30, and top 60 high-income cities were examined. The result was that the top 60 cities yielded −0.505, while the top 30 yielded −0.582. Finally, the top 15 high-income cities generated the highest negative income coefficient of −0.783, which was about 10 times larger than the income coefficient of the 254 cites at −0.074, indicating the existence of a very large-scale advantage of income for PM2.5 pollution was evident in the top 15, top 30, and top 60 high-income cities. This income scale advantage continued through the top 127 cities at a somewhat reduced scale. However, the scale advantage showed a slight scale disadvantage for the remaining bottom 127 cities with lower income per capita. The full sample of 254 cities showed a moderate yet statistically significant income scale advantage by combining different income coefficients from these city subgroups.In summary, a very large-scale advantage of income for reduced PM2.5 concentration, demonstrating that the impact of density on PM2.5 concentrations was positive. When the full sample of 254 cities was divided into the subsample of the top 127 cities with higher density, the density coefficient was more or less unchanged at 0.053, meeting the statistical test of significance, while the effects from the other factors held constant.The same STIRPAT multivariate panel regression was applied first to the full sample of 254 cities, ranked by 2013 population density. The density coefficient from the full sample of 254 cities yielded statistically significant density coefficients of +0.058, as shown in 2.5 pollution was much greater for the group of cities with lower densities compared to the group of cities with higher densities.The bottom 127 cities with lower densities yielded a statistically significant density coefficient of +0.142, which was substantially higher than the density coefficients of both the top 127 and all 254 cities. In other words, the positive impact of population density on PMThe result for the top 60 cities yielded a statistically significant coefficient of +0.05, which was nearly the same as the +0.058 coefficient estimated for all 254 cities. Similarly, the density coefficients for the top 30 cities remained at +0.061. However, for the top 15 cities, the density coefficient more than doubled to +0.13. The density coefficients for the top 15 cities did not meet the statistical test of significance, whereas the density coefficient for the top 30 cities did.2.5 pollution. The positive impact was greater for the 127 cities with lower population densities over both the 127 cities with higher population densities, and the full sample of all 254 cites. The top 60 and 30 cities displayed density coefficients nearly equal to those of the top 127 cities and all 254 cities. However, the top 15 cities displayed a substantially higher density coefficient, approaching the density coefficient derived from the bottom 127 cities. In sum, the overall pattern of density coefficients followed a U-shaped pattern, providing an interesting contrast to the inverse U-shaped pattern of the EKC.In summary, all of the density coefficients displayed a positive impact of density on greater PM2.5 concentrations slightly by 0.018% for the full sample of 254 cities, while the effects of the other factors of income and density were held constant, as listed in 2.5 concentrations.Finally, the full sample of 254 cities ranked by 2013 population size was subjected to the same panel regression analysis. The resulting population coefficient of −0.018 met the statistical test of significance. To explain, a 1% increase in population would reduce PMTo differentiate the degree of impact between large versus small population size, the full sample of 254 cities was again divided into the subgroups of the top 127 largest population cities and the remaining 127 smallest population cities. The resulting population coefficient for the top 127 cities was much smaller at −0.009, compared to the −0.018 estimated for all 254 cities, but the coefficient failed to meet the statistical test of significance. The remaining 127 cities with smaller populations yielded a substantially larger population coefficient of +0.024, which again did not meet the statistical test of significance.2.5 concentrations as the subgroups of the top 127 and all 254 cities. However, only the two population coefficients for the top 15 cities and all 254 cities met the statistical test of significance.To determine the impact of population mega cities of the top 15 most populated cities, the panel regression yielded a statistically significant population coefficient of +0.261. For the subgroup of the top 30 most populated cities, the population coefficient was substantially smaller at +0.027, but failed to meet the statistical test of significance. For the top 60 cites, the population coefficient yielded −0.034, indicating the same population impact on PM2.5 pollution, the results from the subgroup analyses did not support such an impact. On the contrary, the subgroup of 15 mega cities indicated a much higher impact of increasing, not decreasing, PM2.5 pollution. The results from the remaining subgroups were inconclusive.In sum, although an increasing population size for the full sample of 254 cities yielded a moderate reduction in PMIn order to verify the appropriateness of subgroups used in this study so far, the multivariate panel regression was replicated for the four additional subgroups for the respective independent variables. Specifically, we added the subgroups of the top 20, top 50, top 100, and top 200 cities. The four newly estimated income coefficients, all of which are statistically significant, follow the overall declining pattern of coefficients from the top 15 to bottom 127 cities in perfect alignment, indicating the robustness of our previous estimation of the five subgroups. As for the four new density coefficients, they, in general, also support the overall “U” shaped pattern, with a wide flat bottom displayed by the previous five coefficients. The distribution of the four new population coefficients, also, support the overall pattern established by the previously estimated coefficients, where the top 15 displayed the highest coefficients. Similarly, the new coefficients from the subgroup of top 20 cities also displayed the highest coefficients among the new four subgroups.Since both the top 127 largest cities and the bottom 127 smallest cities failed to generate statistically significant population coefficients, we used an alternative model of threshold regression as a robustness check. The results, shown in The population coefficient for Region 1 generated a statistically significant +0.045, compared to −0.009 for the top 127 cities, whereas Region 2 generated a statistically insignificant +0.012, compared to +0.024 for the bottom 127 cities.In sum, the robustness test with threshold regression using the subgroups of alternative population size for the top 94 cities and the bottom 160 cities improved the statistical validity for the subgroup of cities with large populations. However, the basic piecewise linear pattern of population coefficients remained essentially intact.2.5 pollution for the full sample of 254 cities was highest, in that a 1% increase in income generated a −0.074 reduction of PM2.5 concentrations. In contrast, the impact of population density was nearly as high, in that a 1% increase in population density resulted in a 0.058% increase of PM2.5 concentrations. The impact from population size was quite modest, in that a 1% increase of population size resulted in a reduction of only 0.018%.The key findings from this research can be summarized as follows. First, the impact of income measured by city GDP per capita on PMSecond, when all 254 cities were categorized into five subgroups of the top 127, bottom 127, top 60, top 30, and top 15 cities, the impact of income, density, and population varied so widely that each influencing factor needed a separate in-depth analysis. We provide a summary in 2.5 pollution, whereas the bottom 127 lower-income cities with an average income of $30,007 experienced a pollution increase of 0.157% for the same 1% increase in income.Third, the results of the income subgroup analysis showed the most consistent pattern following the EKC. The richest cities displayed the highest scale advantage for greater pollution reduction, whereas the lower income cities experienced a scale of diseconomy with pollution increases. To elaborate, To explain this pattern in the context of the EKC, many cities in the subgroup of cities with an average income of $30,007 had not reached the peak of their EKC, and thus, experienced increasing pollution as their income increased. In contrast, many cities in the subgroup with an average income of $45,537 had surpassed the peak, so experienced a win-win relation of increased income and reduced pollution. Cities with the highest income level experienced proportionately greater pollution reductions, as predicted by the EKC.2 generated the highest density coefficient of +0.142, whereas the top 15 high-density cities also generated an equally high density coefficient of +0.119. In the remaining subgroups, the density coefficient clustered closely around the density coefficient derived from all 254 cities. Thus, the overall distribution of density coefficients resembled a U-shaped pattern, which is opposite to the inverse U-shaped EKC.Fourth, the results of the density subgroup analysis showed a somewhat opposite pattern from the income subgroups. As shown in As noted in the earlier section of the literature review, many cities in the U.S and some European countries with low-density urban sprawl may have been responsible for the unusual high-density coefficient of +0.142 estimated for the subgroups of the bottom 127 cities. For example, the bottom 127 subgroup contained a large minority of 36 American cities. Furthermore, this subgroup contained 14 American cities in the bottom 20 lowest density cities, indicating the impact of low-density sprawl cities.2 may reflect the fact that extremely high-density cities will begin to experience excessive spatial concentration and consequently increasing vehicle emissions due to severe congestion as well as the high number of people exposed to pollution. These can bring about a rapidly rising air pollution. Furthermore, the fact that there are seven high-density Japanese cities included in the top 15 subgroup may have generated another cause for the unusually high coefficient derived for this subgroup.The high-density coefficient of 0.119 from the subgroup of the top 15 cities with a very high average density of 4010 inhabitants per km2.5 pollution. The population coefficient from the remaining subgroups clustered around the population density of the full sample of all cities group. Therefore, the distribution of population coefficients can be approximated using a piecewise linear relation, as displayed in 2.5 pollution. The only exception was in the case of the most populous 15 mega cities with an average population size around 10 million inhabitants.Fifth, the results from the population subgroups were somewhat inconsistent and contradictory in that only the full sample of 254 cities and the top 15 most populous mega cities generated statistically significant population coefficients. All 254 cities generated −0.018, while the top 15 subgroup generated +0.261. In other words, most cities would experience a very modest pollution reduction in the full sample of 254 cities, whereas the most populous cities in the top 15 subgroup would experience the largest increase in PM2.5 pollution between the two groups was quite similar, as both the previous studies and our study verified the theory of EKC. One difference may be that the inflection point of the EKC in China could be somewhat lower in the range of $9186 to $18,506 per capita [It would be interesting to compare the results of this study to the results of studies on Chinese cities discussed earlier in the literature review section. First, the impact of income on PM2). In comparison, the average population density for 285 cities in China during a similar study period of 2001 to 2012 was estimated at a much higher density of 1149.86 persons per km2 [2.5 pollution was inconclusive, although our study revealed rapidly increasing pollution from the most populous cities with an average of 10 million inhabitants.As for population density, both groups of studies verified the result of increasing pollution as a function of increasing population density. However, the U-shaped distribution of density coefficients revealed in this study may be due to many low-density urban sprawls found particularly in the U.S. For example, the average density for all 254 cities was quite low  a medium population size between 1.5 million to 4 million inhabitants. In contrast, the worst combination of the three factors are (1) low-income cities with significantly less than $30,000 per capita; (2) the highest population density of more than 4000 persons per km2; and (3) the largest population size of more than 10 million inhabitants.The findings of this study have policy implications for all countries. An ideal combination of the three influencing factors examined in this study that are most favorable to pollution reduction are (1) an average income per capita of $38,000 or more; (2) population density in the range of 1000 to 2000 population per km2, while the indifference zone of population size ranges from 1.3 million to 6.4 million inhabitants.We realize, however, that such an ideal combination would be quite difficult to achieve in most cities. Fortunately, the results of this study have identified a rather wide indifference zone of the average values in all three factors. For the income, any city income per capita over $38,000 would generate a substantial pollution reduction. For density, the wide indifference zone ranges from about 700 to 3000 persons per km2.5 pollution that have been analyzed in previous studies. For example, several previous studies on Chinese cities have included other influencing factors such as industrial structure, traffic intensity, energy and electricity usage, and coal consumption [2.5 pollutions, rather than evidence of causality. Thus, future work should continue to establish the causal relationships to control air pollutions.There are several limitations to this study that represent possible topics for future studies. One major limitation is the omission of several socio-economic factors related to PMsumption ,4,7. Anosumption . Other osumption ,64,65,66Despite these limitations, this research revealed the role of high-income cities in developed countries and added insights about how pollution reduction can have a greater impact compared to developing countries such as China. This study also supports the positive impact of high population density cities on increasing pollution, which is also the case in developing countries. Going beyond this basic notion, this study proposes a U-shaped pattern of density coefficients as a function of variable population densities of cities for developed countries.


The biocatalyst was incorporated into biocatalytic cascades and chemo-enzymatic strategies to synthesise cello-oligosaccharides with tailored functionalities on a scale suitable for employment in mass spectrometry-based assays. The resulting glycan structures enabled reporting of the activity and selectivity of celluloltic enzymes.Promiscuous activity of a glycosyltransferase was exploited to polymerise glucose from UDP-glucose  via the generation of β-1,4-glycosidic linkages.Promiscuous activity of a glycosyltransferase was exploited to polymerise glucose from UDP-glucose  As the major structural component of plant cell walls, it is exploited as a renewable resource in the bio-economy, enabling sustainable production of fuels and chemicals.4 Enzymatic synthesis of cellulose in vitro is challenging because the natural biosynthetic machinery consists of membrane-embedded, multi component systems.5 Native and derivatised cellulose (oligosaccharides) have been generated from glucose-1-phosphate and cellobiose via the reversible reaction mechanism of cellodextrin phosphorylases, whereby reaction conditions can direct the degree of polymerisation (DP).6–8 Furthermore, exploitation of enzymes that utilise activated glycosyl donors (e.g. glycosyl fluorides) such as glycosynthases, have opened up a raft of new options for synthesising glycosides.9–11 However, such systems require unnatural donors or are biased towards production of very long oligomers. Despite these advances, it remains challenging to generate soluble cello-oligosaccharides from natural donors due to the limited availability of suitable biocatalysts.Chemical synthesis of cellulose derivatives is complex due to the required stereo- and regioselectivity.12 Enzymatic synthesis of Glc-β-1,4-Glc linkages from nucleotide sugars has been restricted to plant and bacterial cellulose synthases. However, GTs can be promiscuous in acceptor and donor substrates, thereby providing biocatalysts with potentially exploitable side reactions with rates that in many cases were demonstrated to be sufficient for exploitation in glycoside synthesis.13–15 Therefore, an GT able to accommodate both UDP-Glc as donor and a Glc-terminated structure as acceptor would in effect function as glucose polymerase (via reaction optimisation or enzyme engineering.Glycosyltransferases (GT) synthesise highly regio- and stereospecific glycosidic bonds between glycan acceptors and activated sugar donors.lymerase , wherebyin vitro. We demonstrate the synthesis of cello-oligosaccharides and their derivatives to a scale suitable for mass spectrometry-based detection and exemplify how these compounds facilitate the profiling of hydrolytic and oxidative biomass-degrading enzyme activities.Here we describe how the rational exploration of galactosyltransferase substrate promiscuity resulted in the identification of a broad-specificity biocatalyst that functions as a glucose polymerase 16 enzymes with a variety of reported specificities were included.With the aim to identify a GT capable of generating β-1,4-linked glucose (Glc) oligosaccharides, we assembled a panel of five recombinantly expressed galactosyltransferases and screened it for promiscuous acceptance of Glc-based donor and acceptor substrates. Transfer of UDP-Glc to an acceptor with a terminal Glc-R motif would result in reaction products that can be re-used as an acceptor, thus enabling the desired Glc polymerisation. As the acceptor structure can potentially affect the generated glycosidic linkage type,15,17 Glc-ITag-1 (1)  benzyl β-glucoside Glc-ITag-2 (2) , such as 4-(1-methyl-3-methyleneimidazolium)benzyl carbamate β-glucosideag-1 (1) . Such ca Fig. S1A in 4 ste Fig. S1A with a y Fig. S1A. In cont1 or 2 and an excess of UDP-Glc at 37 °C, a range of ITagged glucose oligosaccharides with DPs of up to 7 and 9, respectively  confirmed that the Glc residues were connected by β-1,4-glycosidic linkages -pNP , Man-pNP, Glc(-pNP) and various C2-derivatives.13–15,20 No activity was found using Gal, xylose (Xyl), arabinose (Ara), lactose , trehalose  or UDP-Glc as acceptors. Acceptor substrates thus require an equatorial configuration of the C4 –OH group and the presence of a C6 –OH group while both the C1 and C2 substitutions are highly flexible. Limited transfer of Xyl and GalNAc but not GlcNAc from their respective UDP-conjugates to acceptors was also detected 21, hereafter SuSy. In the presence of UDP, SuSy converts relatively inexpensive sucrose into fructose and UDP-Glc, this biocatalyst is therefore widely employed for the synthesis of nucleotide sugars and glucosides.22 Incubation of SuSy with LgtB, sucrose and 1 or 2 resulted in formation of ITagged cello-oligosaccharides , copper dependent enzymes that oxidatively degrade oligosaccharides and polysaccharides. LPMOs insert a single oxygen atom into the C1–H and/or C4–H bond of saccharide substrates, ultimately producing aldonic acids or 4-gemdiol-aldoses, respectively A,23Neurospora crassa NcLPMO9C25 and Thermobifida fusca Tf(AA10)B26 (see ESI2O2 and ascorbate as a reducing agent. The distribution of labelled oligosaccharides was unchanged following incubation with Tf(AA10)B (Nc(AA9)C and Ls(AA9)A led to complete consumption of oligosaccharides with DP ≥ 6 and 4, respectively (15 that complicate interpretation30 of MS-based detection of LPMO products. Selective detection of endo-cellulase activity in glycoside hydrolase (GH) preparations is in principle enabled via an enzyme substrate that cannot be hydrolysed from its termini. Exploiting LgtB, we generated such a ‘blocked’ substrate probe compatible with fast and sensitive detection of enzyme activity via MS. Initially we confirmed commercially available cellulolytic GHs degrade unmodified ITagged cello-oligosaccharides B , confirmectively , with coectively . These dcharides . ‘Blockeh see ESI via prev Fig. S12.via MS.With the blocked substrates in hand, we confirmed their protection against exo-acting enzyme activity. Terminal galactose addition was sufficient to protect cello-oligosaccharides against β-glucosidase activity, while hydrazide derivatisation was required and sufficient to protect structures against a set of β-galactosidases . Finallyvia β-1,4-glycosidic linkages onto a variety of acceptors containing chemical handles, producing functionalised, tailored cello-oligosaccharide derivatives as detectible by mass spectrometry. We demonstrated the incorporation of LgtB-mediated glucose polymerisation into chemo-enzymatic derivatisation strategies, via production of probes to selectively detect endo-acting cellulolytic enzyme activities in a sensitive, MS-based assay. Further work regarding scalability of the LgtB reaction including determination of isolated yields should confirm if LgtB would be suitable as catalyst for glucose polymerisation in reactions at preparative scale and upwards. As LgtB can be obtained via facile heterologous expression in E. coli, this biocatalyst for the synthesis of β-1,4-glycosidic linkages is very well suited to be engineered to accept desired non-natural substrates, which may open up novel routes to enzymatic synthesis of cellulose derivatives.In conclusion, LgtB has a broad substrate scope that includes polymerase activity with UDP-Glc as donor and Glc-terminated glycosides as acceptors. We successfully exploited this promiscuous activity to polymerise glucose There are no conflicts to declare.OB-019-D1OB00971K-s001


A total of 263 different alleles were determined across all 27 Y-STRs with the corresponding allelic frequencies from 0.0004 to 0.7401, and the range of genetic diversity (GD) was 0.4027 (DYS391) to 0.9596 (DYS385a/b). In the first batch of 27 Yfiler data in Maoming Han, 417 distinct haplotypes were discovered, and nine off-ladder alleles were identified at six Y-STRs; in addition, no copy number variant or null allele was detected. The overall haplotype diversity (HD) and discrimination capacity (DC) of 27 Yfiler were 0.9997 and 0.9675, respectively, which demonstrated that the 6-dye and 27-plex system has sufficient system effectiveness for forensic applications in Maoming Han. What is more, the phylogenetic analyses indicated that Maoming Han, which is a Southern Han Chinese population, has a close relationship with Meizhou Kejia, which uncovered that the role of the gene flows from surrounding Han populations in shaping the genetic pool of Maoming Han cannot be ignored. From the perspectives of genetics, linguistics, and geographies, the genetic structures of Han populations correspond to the patterns of the geographical-scale spatial distributions and the relationships of language families. Nevertheless, no exact genetic evidence supports the intimate relationships between Maoming Han and Tai-Kadai language-speaking populations and Han populations of Central Plains in the present study.Maoming is located in the southwest region of Guangdong Province and is the cradle of Gaoliang culture, which is the representative branch of Lingnan cultures. Historical records showed that the amalgamations between Gaoliang aborigines and distinct ethnic minorities had some influences on the shaping of Gaoliang culture, especially for the local Tai-kadai language-speaking Baiyue and Han Chinese from Central China. However, there is still no exact genetic evidence for the influences on the genetic pool of Maoming Han, and the genetic relationships between Maoming Han and other Chinese populations are still unclear. Hence, in order to get a better understanding of the paternal genetic structures and characterize the forensic features of 27 Y-chromosomal short tandem repeats (Y-STRs) in Han Chinese from Guangdong Maoming, we firstly applied the AmpFLSTR Nianli  and Piaose (a form of dramatic plastic arts on moving stages) (Gaoyang Pian), which is one branch of Sino-Tibetan language family  . Since t stages) 12). Ther stages) . Moreove stages) . Maominge family . From th® Yfiler® Plus PCR Amplification Kit  includes 17 Yfiler loci  plus three highly polymorphic Y-STR loci  and seven rapidly mutating Y-STR loci  in an effort to improve discrimination of related individuals  have been regarded as a valuable tool in forensic genetics , genealoividuals . Y-chromgenetics . As yet,Hence, in order to get a better understanding of the paternal genetic structure and characterize the forensic resolution of 27 Y-STRs in Han Chinese from Guangdong Maoming, we used the 6-dye and 27-plex Y-STR system to genotype the haplotypes in 431 Han males residing in Maoming city. Furthermore, we explored the genetic relationships between Maoming Han and Chinese populations of Southern and Northern China from the perspectives of geographies, linguistics, and genetics.TM, GE Healthcare, Chicago, IL, United States) with written informed consents from participants. All the experimental procedures were performed following the standards of the Declaration of Helsinki. This study was approved by the Medical Ethics Committee of Hainan Medical University (no. HYLL-2020-012).In this study, a total of 431 unrelated Han Chinese males were recruited from Maoming city, Guangdong Province, China . The incTM dsDNA HS Assay Kit  on the Qubit 4.0 Fluorometer  according to the manufacturer’s instructions. Based on the quantitative results, DNA samples were normalized to 2.0 ng/μl and stored at −20°C until amplification.Genomic DNA was extracted using the TIANamp Blood Spot DNA Kit  according to the manufacturer’s protocol. The quantity of the DNA templates was determined using Qubit® Yfiler® Plus PCR Amplification Kit  was performed in a single multiplex PCR reaction  on a Veriti® 96-Well Thermal Cycler System  following the manufacturer’s instructions. Amplified products were separated by capillary electrophoresis (CE) on a 3500xL Genetic Analyzer . The separation of CE-based amplified products was conducted according to our previous studies , haplotype diversity (HD), discrimination capacity (DC), and random match probability (RMP). GD was calculated according to the following formula:n is the total sample size, and p_i indicates the frequency of i-th allele. HD was computed in the same formula as GD, except that p_i refers to the frequency of i-th haplotype. DC is equal to the ratio of different haplotypes to the total sample size. Computed with the formula where Rst) is commonly used for estimating the population differences and computing the genetic relationships among different populations  and visualized in multidimensional scaling (MDS) plot, which were used show the reduced dimensionality spatial representation of the populations. Additionally, phylogenetic relationships among Han Chinese populations from Southern and Northern mainland China as well as those between 6 Han Chinese and 16 ethnic minorities were depicted in the Molecular Evolutionary Genetics Analysis-X (MEGA-X) software . The haplotype data of 431 unrelated male individuals from Guangdong Maoming Han population in the present study have been submitted to YHRD database and received the accession number YA004720 . The Y-STR profiles with off-ladders were re-amplified and re-genotyped by Goldeneye DNATM ID 27YB system .The recommendations of the DNA Commission of the Chinese National Standards, the Scientific Working Group on DNA Analysis Methods (SWGDAM) , and the® Yfiler® Plus PCR Amplification Kit . In order to evaluate the forensic features of Maoming Han population, we set up two datasets, Yfiler set and Yfiler Plus set, including 17 and 27 Y-STRs, respectively. In addition, a series of comprehensive population genetic analyses were conducted between Maoming Han and other southern and northern Chinese populations. In short, the aims of this study were to feature the forensic characteristics of 27 Y-STRs in Maoming Han, clarify the paternal genetic structures of Maoming Han, and get a better understanding of the genetic relationships between Maoming Han and other Chinese populations from the perspectives of geographics, linguistics, and genetics.In the present study, a total of 431 unrelated male individuals from Han Chinese in Guangdong Maoming were genotyped including 27 Y-STR loci using the AmpFLSTRAs illustrated in Allele frequency distributions and haplotype frequencies of Yfiler Plus for Maoming Han are presented in In this study, duplicated or triplicated alleles and null alleles were not detected in both Yfiler set and Yfiler Plus set. The analysis of genotype data revealed that DYS385a/b and DYF387S1a/b showed higher GD in Maoming Han, which were the same as other Chinese populations . ForensiRst and corresponding p values based on the 17 Yfiler among Maoming Han and 11 other Han Chinese populations from North to South across the mainland China (p = 0.05) except for Guangdong Han. However, after Bonferroni’s correction , there were differences between Maoming Han and Northern Han Chinese from Gansu  and Shandong , while other Han Chinese populations had no significances with Maoming Han, especially for Guangdong Han , indicating the genetic affinity between Southern Han and Northern Han in mainland China. The MDS plot , were grouped in the bottom left side, while Hainan, Hubei (Wuhan), Hunan, Guizhou (Zunyi), and Guangdong isolated from the northern cluster and gathered into a Southern Han cluster in the upper right side with Maoming Han.From the intercontinental perspective, a MDS was performed between Maoming Han and 21 worldwide populations . As shownd China  are listMDS plot  based onRst values was constructed  and Hainan Han . The phylogenetic structures of Han Chinese populations from Southern and Northern mainland China in the phylogenetic dendrogram were in line with the results of the MDS. From the geographical scale, Guangdong, Guangxi, Hainan, Guizhou, and Hunan belong to southern administrative divisions of mainland China, while Jilin, Gansu, Shandong, Hebei, and Henan are subordinated to the northern administrative divisions of mainland China , while significant genetic differences were observed between Maoming Han and all other Han Chinese or ethnic groups (p < 0.05). However, after Bonferroni’s correction , there were no differences between Maoming Han and surrounding Han Chinese populations (Rst = 0.1614), Xinjiang Uighur (Rst = 0.1407), and Lhasa U-Tsang Tibetan (Rst = 0.1294) in Northern China had the longest genetic distances with Maoming Han, while the closest genetic distance was seen in Meizhou Kejia (Rst = 0.0007), followed by Guangxi Han (Rst = 0.0031), Hainan Han (Rst = 0.0033), then by Jieyang Han (Rst = 0.0039), and Chaoshan Han (Rst = 0.0083).According to the history records, the population structures of Maoming Han were mainly affected by the intermarriages with local Tai-kadai language-speaking Baiyue population and the south migrations of Han Chinese from Central China 12), whilulations . FurtherRst values of 22 Chinese populations, an MDS plot  demonstrated that Maoming Han was isolated from Chinese ethnic minority groups relatively and had a relatively close genetic relationships with Southern Han populations, especially for those with the same dialects and intimate geographical distances , which indicated that there might be gene flows between Maoming Han and the surrounding Han populations. In addition, the genetic structures of Han populations correspond to the patterns of the geographical-scale spatial distributions and the relationships of language families. In total, the results of above population genetic analyses indicated that Maoming Han, which is a Southern Han Chinese population, has a relatively close relationship with Meizhou Kejia; therefore, the role of the gene flows from surrounding Han populations in shaping the genetic pool of Maoming Han cannot be ignored.® Yfiler® Plus PCR Amplification Kit . The high-quality 27 Y-STR haplotype data of Maoming Han were obtained and submitted to YHRD with the accession number YA004720. Overall, 263 different alleles were identified across all 27 Y-STRs with the number of distinct alleles from 4 to 55. Allele frequencies varied from 0.0004 to 0.7401, and the lowest and highest estimates of GD corresponded to loci DYS391 (0.4027) and DYS385a/b (0.9596), respectively. Genotyping with the 27 Y-STRs determined 417 distinct haplotypes in the population of Maoming Han, of which 405 (97.12%) were unique. In the first batch of 27 Yfiler data for Maoming Han, nine intermediate alleles were detected at six Y-STR loci; in addition, duplicated or triplicated alleles and null alleles were not observed. Based on the comparisons of forensic parameters for different sets of Y-STRs (17 Yfiler set and 27 Yfiler Plus set), it demonstrated that the improvements of HD and DC are accompanied by the increasing numbers of Y-STRs. The overall HD and DC of 27 Yfiler in Maoming Han were calculated to be 0.9997 and 0.9675, respectively.In the present study, a total of 431 unrelated Guangdong Maoming Han were investigated using the AmpFLSTR® Yfiler® Plus PCR Amplification Kit in Maoming Han demonstrated that it can be widely applied in the population of Guangdong Maoming Han for forensic purposes, and Maoming Han, which is a Southern Han Chinese population, and has a relatively close relationship with Meizhou Kejia; therefore, the role of the gene flows from surrounding Han populations in shaping the genetic pool of Maoming Han cannot be ignored.From the perspectives of genetics, linguistics, and geographies, different Han Chinese populations from distinct administrative divisions of mainland China have genetic and linguistic homogeneousness, and the genetic distances and population structures of Han Chinese are in accordance with the geographical-scale pattern to a certain extent in mainland China. Maoming Han, a Southern Han population, has a relatively close genetic relationship with Meizhou Kejia, which has the same language family and has intimate geographical distances with Maoming Han, while no exact genetic evidence supports that there are intimate relationships between Maoming Han and Tai-Kadai language-speaking populations and Han populations of Central Plains. At the same time, we found that the genetic structures of Han populations correspond to the patterns of the geographical-scale spatial distributions and the relationships of language families. As a whole, the sufficient systematic efficiencies of AmpFLSTRThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The studies involving human participants were reviewed and approved by Medical Ethics Committee of the Hainan Medical University. The patients/participants provided their written informed consent to participate in this study.HF made significant contributions in the conceptualization, resources, software, formal analysis, and project administration. YL and QX made significant contributions in the investigation. LW performed the validation. QX performed the data curation. HF and QX performed the visualization, wrote and prepared the original draft, and reviewed and edited the manuscript. PQ and S-QW made significant contributions in the supervision of the study. PQ acquired funding for the study. All authors reviewed 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. The handling editor declared a past co-authorship with the authors LW and S-QW.


Hosta, also Asia) that are very diverse in their ecology and comprise 12 genera and ca. 445 species. Species in the Agavoideae display impressive adaptions in order to live in arid and semiarid conditions, have been historically very important for local human populations, and today are gaining increased attention due to their immense biotechnological potential  are a group of charismatic plants native to the Americas  from the Mojave desert that are pollinated by different Tegeticula moth species. The Yucca species present different sizes, with Y. brevifolia taller and with larger flowers than Y. jaegeriana, and morphological differences exist between their pollinators as well. Royer et al. analyzed a narrow (ca. 4 km in width) hybrid zone between these species in the Tikaboo Valley, southern Nevada, using RAD-seq data, microsatellite loci, chloroplast variation, vegetative and floral traits, and pollinator frequency. They found overlapping genomic and pollinator clines, consistent with a narrow hybrid zone generated by strong selection, but with wider phenotypic and a chloroplast clines.Papers in this collection focus on the two largest genera in the Agavoidae: Arteaga et al. studied the populations of two closely related Yucca species: Y. valida that lives mostly in the central section of the peninsula and Y. capensis that is only found in the southern tip of the peninsula. They confirmed the hybrid origin of geographically and morphologically intermediate populations using nextRAD derived SNP along Principal Components and Structure analysis and Approximate Bayesian computation simulations. This conclusion was supported by the distribution models constructed using climatic data for the present and the past.In the Baja California peninsula, Heyduk et al. studied the hybrid origin of Y. gloriosa, a putatively hybrid species between Y. aloifolia and Y. filamentosa, which is photosynthetically intermediate  that helped to cultivate agaves by harvesting during rainfall and retaining soil moisture.The Cabrera-Toledo et al. analyzed the levels of morphological and genetic variation and differentiation patterns in A. maximiliana, used in the production of raicilla, a local kind of mescal in Western-Central Mexico. The authors found strong morphological and genetic differentiation between populations (FST = 0.43), and isolation by distance in the genetic markers. The authors compared plants in different management categories , but they did not find differences in morphology nor in genetic diversity among these categories. Torres-García et al. studied the demography of A. inaequidens, another species used in the production of raicilla and other types of mescal, across four wild populations in Central Western Mexico. The study indicated that analyzed wild populations are stable, concluding that extraction rates from 10 to 30% of mature individuals for making mescal could be sustainable, but only if 200 to 300 agave plants of the younger sizes are introduced every year into the populations.Trejo et al. They analyzed vegetative traits of landraces of A. salmiana and A. mapisaga in Tlaxcala, a highland state in central east Mexico, and their genetic relationships using a chloroplast region (trnL) and nuclear ITS sequences. Both, data on the morphology and genetic markers of the landraces aligned with the species classification, with the exception of A. salmiana subsp. salmiana “Ayoteco,” which is more related to A. mapisaga var. mapisaga. They concluded that low intensity artificial selection together with gene flow and plasticity could explain the high number of phenotypically similar landraces.Another group of agaves used for the production of pulque–a traditional Mexican alcoholic beverage produced through open fermentation of agave sap—was studied by Agave kerchovei has a restricted distribution, mainly in the Tehuacán -Cuicatlán Valley, in the states of Oaxaca and Puebla. Aguirre-Planter et al. studied the chloroplast variation and both current and past ecological niche models, finding high levels of total chloroplast genetic variation and very strong genetic differentiation . They suggest that the Pleistocene glacial cycles played a critical role in changing the distribution and population sizes of the species.Tegeticula moth in Yucca and the nectar feeding Leptonycteris bats in Agave; Pellmyr and Leebens-Mack, A. lechuguilla, in Cuatro Ciénegas, in the Chihuahuan desert, López-Lozano et al. analyzed the microbial communities in the soil and in its rhizosphere using 16S rRNA gene sequences. Actinobacteria were more abundant in the soil, while Proteobacteria dominated in the rhizosphere. The authors reported differences in bacterial diversity, community composition, potential functions of the bacteria taxa, and interaction networks between the soil and A. lechuguilla rhizosphere.The interaction with other species seems to be very important in the evolutionary ecology of agavoid species, in particular mutualistic pollinator interactions . The authors also used this gene to identify accessions closely related to A. tequilana var. azul. Agaves store their carbohydrates in the form of fructan polymers, instead of starch or sucrose. Pérez-López and Simpson describe the fructans in Agave that provide a source of carbohydrates for the transition from vegetative to reproductive stages; for this reason, inflorescences are removed to avoid depletion of fructan reserves in cultivated and managed populations of Agave.The plants of the Agavoideae have many other physiological and biochemical adaptations and unique chemical profiles. Agave for living in dry areas are highlighted in the study of Jones et al. where they conducted field experiments in A. americana to evaluate their ethanol yield potential, analyzing their efficiency in water use, their carbohydrates production, and the products obtained by enzymatic hydrolysis comparing with other plants—grasses–used to produce biofuels. They concluded that A. americana was able to produce important amounts of soluble carbohydrates by using very little water, and thus have an important potential for future biofuel production.The striking physiological adaptations of González-Gutiérrez et al. that describes how to analyze the F-actinin in plant species with thick tissues, as is the case for Agavoideae plants.Finally, this collection also includes a methodological paper by This collection analyses only a few species, adaptations and uses from a very large group of fascinating plants, including agaves called “el árbol de las maravillas” (the tree of wonders) by José de Acosta (de Acosta, LE, JL, and KH proposed the Research Topic, edited manuscripts, and wrote the editorial. All authors contributed to the article and approved the submitted version.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 reviewer CM declared a shared affiliation, with no collaboration, with one of the authors, LE, to the handling editor at the time of review.


This data article provides a descriptive overview of the Cities and Armed Conflict Events (CACE) dataset and the data collection methods. The dataset provides a systematic coding of armed conflict events taking place in cities and outside cities across the globe. It constitutes an extension of the Uppsala Conflict Data Program (UCDP) Georeferenced Events Dataset (GED) version 18.1 and covers 1989–2017. To identify which events of armed conflict took place in cities, the data was manually matched to to data from the United Nations Statistics Division (UNSD). The dataset enables systematic analysis of urban-rural patterns in armed conflict, as illustrated by Elfversson & Höglund  This new category of information enables studies of urban/rural patterns in violence that rely on a more valid measure of cities than approaches based on spatial grids combined with population density.•The data will be useful for studies on spatial conflict dynamics, particularly for studies that focus on cities and urban dynamics as a unit of analysis or predictor.•For example, the data may be used to study under what conditions conflicts are likely to affect cities, if conflicts become more or less urban over time, and if different forms of organized violence affect cities differently.1The dataset “Cities and armed conflict events” consists of two data files  and a Codebook which provides information on the variables contained in the data file. The dataset constitutes an extension of the Uppsala Conflict Data Program (UCDP) Georeferenced Events Dataset (GED) In total, the dataset contains 142 902 conflict events. According to CACE, 24 904 of the armed conflict events (17%), and 294 158 (15%) of the total fatalities, took place in cities. The UCDP collects data for three categories of organized violence: state-based conflict, non-state conflict, and one-sided violence against civilians. The first category includes armed conflict between states, as well as armed conflict between a state and one or more rebel groups. For an in-depth analysis of urban-rural patterns in state-based conflict, see Elfversson & Höglund The data can also be used for comparison across different cities. 2To identify armed conflict events taking place in cities, I matched the UCDP GED In the first step, I identified all countries with UCDP GED events, including states that have formed since 1989 from larger states which experienced UCDP GED events . Next I extracted all cities in these countries from the UNSD data. I included only those cities that had at least 100 000 inhabitants in the city proper at some point since 1989. This numerical threshold was chosen because population centers of this size are expected to feature characteristics associated with cities: density, heterogeneity, high economic activity, and a certain political importance. Additionally, I included national capitals which did not meet this threshold .Given concerns about the reliability of city statistics – which include issues of politicized national censuses, poor infrastructure for gathering population statistics, and country variations in precisely how cities are delimited  all cities with a recorded  population of at least 100 000 at some point since 1989, contains a total 3956 cities.In the next step, I matched the cities on the list to the UCDP GED (v 18.1). All the events contained in the UCDP GED were coded according to whether or not they occurred in one of the cities on my list, based on the location description in the dataset (“where”). In UCDP coding procedure, “where” is standardized so that all events taking place within a certain city are assigned to that city, rather than to a city sub-location  indicates whether “where” matches a city according to my operationalization. Out of the total 3956 cities identified at the outset, 974 saw at least one event of organized violence as defined and coded by the UCDP. A separate dichotomous variable (CAPITAL) indicates if the event took place in a capital city. Furthermore, I include two additional dichotomous variables, MAJORCITY and TOP3CITIES which denote cities with a population of at least 750 000, and the three largest cities in each state, respectively.Just as the underlying UCDP GED dataset, the CACE dataset covers the period 1989–2017, and is global in scope .The author declares that she has no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article.