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Men of African descent have the highest prostate cancer incidence and mortality rates, yet the genetic basis of prostate cancer in African men has been understudied. We used genomic data from 3,963 cases and 3,509 controls from Ghana, Nigeria, Senegal, South Africa and Uganda to infer ancestry-specific genetic architectures and fine-map disease associations. Fifteen independent associations at 8q24.21, 6q22.1 and 11q13.3 reached genome-wide significance, including four new associations. Intriguingly, multiple lead associations are private alleles, a pattern arising from recent mutations and the out-of-Africa bottleneck. These African-specific alleles contribute to haplotypes with odds ratios above 2.4. We found that the genetic architecture of prostate cancer differs across Africa, with effect size differences contributing more to this heterogeneity than allele frequency differences. Population genetic analyses reveal that African prostate cancer associations are largely governed by neutral evolution. Collectively, our findings emphasize the utility of conducting genetic studies that use diverse populations.

Reported Lyme disease cases have been rapidly increasing globally in recent years. Despite this, few studies have explored how the public perceives health risks associated with ticks and Lyme disease, especially within the context of visitor experiences in natural areas. To address this knowledge gap, we conducted a survey of visitors to one of Ontario, Canada’s most visited provincial parks. Despite high perceptions of risk, few visitors feel sufficiently educated about ticks and Lyme disease. Further, only 51% of visitors would return to the park if future regional incidents of tick-borne illnesses increased by 10–30% (under climate change). Interestingly, no significant correlations were uncovered between home distance and tick-related risk perceptions or self-reported tick education levels. These results may be indicative of the normalization of tick-related risk throughout many of southern Ontario’s most populated regions. Finally, our results also revealed that there is no preferred “one-size-fits-all” risk communication strategy that can be used to support risk-reducing behaviour change in a natural areas context. We recommend a diversity of evidence-based, on-site, and off-site strategies, that can be considered by governing authorities not only in Canada but indeed globally to reduce tick-related health and safety risks and ultimately safeguard visitor experiences in natural areas subject to increasing tick prevalence.

Childhood and adolescence are crucial periods for mental and social development. Currently, mental illness among young people is a global epidemic, and rates of disorders such as depression and anxiety are rising. Urban living, compared with rural living, is linked with a higher risk of serious mental illness, which is important because the world is urbanizing faster than ever before. Urban environments and their landscapes, designs, and features influence mental health and well-being. However, no conceptual frameworks to date have detailed the effect of urban environments on young people’s mental health, and few studies have considered the growing role of digital and social media in this relationship, leading to calls for the development of holistic approaches to describe this relationship. This article synthesizes existing knowledge on urban places (both built and natural environments) and mental health in the public health and urban planning literature and examines the emerging field of neurourbanism (a multidisciplinary study of the effect of urban environments on mental health and brain activity) to enhance current practice and research. We developed 2 novel conceptual frameworks (1 research-oriented, 1 practice-oriented), adapted from Bronfenbrenner’s socioecological model, that focus on the relationship between urban environments and young people’s mental health. We added a digital and social media contextual level to the socioecological model, and we applied a multilayer concept to highlight potential cross-field interactions and collaborations. The proposed frameworks can help to guide future practice and research in this area.

Hutchinson–Gilford progeria syndrome (HGPS or progeria) is typically caused by a dominant-negative C•G-to-T•A mutation (c.1824 C>T; p.G608G) in LMNA, the gene that encodes nuclear lamin A. This mutation causes RNA mis-splicing that produces progerin, a toxic protein that induces rapid ageing and shortens the lifespan of children with progeria to approximately 14 years1–4. Adenine base editors (ABEs) convert targeted A•T base pairs to G•C base pairs with minimal by-products and without requiring double-strand DNA breaks or donor DNA templates5,6. Here we describe the use of an ABE to directly correct the pathogenic HGPS mutation in cultured fibroblasts derived from children with progeria and in a mouse model of HGPS. Lentiviral delivery of the ABE to fibroblasts from children with HGPS resulted in 87–91% correction of the pathogenic allele, mitigation of RNA mis-splicing, reduced levels of progerin and correction of nuclear abnormalities. Unbiased off-target DNA and RNA editing analysis did not detect off-target editing in treated patient-derived fibroblasts. In transgenic mice that are homozygous for the human LMNAc.1824 C>T allele, a single retro-orbital injection of adeno-associated virus 9 (AAV9) encoding the ABE resulted in substantial, durable correction of the pathogenic mutation (around 20–60% across various organs six months after injection), restoration of normal RNA splicing and reduction of progerin protein levels. In vivo base editing rescued the vascular pathology of the mice, preserving vascular smooth muscle cell counts and preventing adventitial fibrosis. A single injection of ABE-expressing AAV9 at postnatal day 14 improved vitality and greatly extended the median lifespan of the mice from 215 to 510 days. These findings demonstrate the potential of in vivo base editing as a possible treatment for HGPS and other genetic diseases by directly correcting their root cause.

Several letters to the editor are presented in response to articles in previous issues, including "AIDS and Business" in the March 28, 1987 issue, "The U.S. Can't Compete Without a Topnotch Work Force" in the February 16, issue and "Pick an HMO Carefully: Your Life May Depend on It" in the March 2, 1987 issue.

AbstractOver recent years, the rapid growth of big data and associated analytical tools has provided unparalleled opportunities for retailers to enhance their location decision support activities. To date, there is a lack of research that looks at how retail firms are leveraging such innovation in data and technology. Based on an online survey conducted with Canadian retail location decision makers, this article examines the current state and evolution in (1) the type and scale of location decisions that retail firms undertake; (2) the availability and use of technology and geospatial data within the decision-making process; and (3) the range of location research methods that are employed. The findings highlight that there has been a widespread increase in the availability and use of technology and geospatial data within the decision-making process. The range of analytical approaches has also expanded to include methods that work with new data sources, such as social media and mobile device location data. The adoption and development of big data approaches is also challenged, however, by factors such as information hoarding, a lack of understanding and buy-in from senior management, and a lack of skilled analysts who can manage and synthesize the big data.

ABSTRACTWe conducted a scoping review to determine incidence and risk factors for postpartum haemorrhage (PPH) in rural Indigenous women. We systematically searched PubMed (Medline), EMBASE, and CINAHL for all peer-reviewed articles and grey literature regarding Indigenous ethnicity, rural settings, and PPH incidence, risk factors, or maternal outcomes published from inception to 11 January 2021. Eleven articles were deemed relevant after screening and quality assessment using the National Institutes of Health scoring system for mixed study reviews. Of these, 3 articles were good quality, 1 was fair, and 7 were poor. Nine possible risk factors were recorded. The outcomes studied were transfusion, hysterectomy and mortality. PPH research in rural Indigenous women is scarce, mostly low quality and fails to represent most Indigenous cultures and countries. Women from Indigenous groups in rural Canada, Australia and the USA are at higher risk for PPH but specific risk factors are unknown. While widely differing populations made the data difficult to synthesise, this inaugural scoping review highlights a need for further research and increased obstetrical resources in areas where rural Indigenous women reside.

Lyme disease is the most common vector-borne disease in North America. Despite this, there is a limited understanding of public perceptions and behaviours associated with tick and Lyme disease risk, especially within a parks and protected areas visitation and visitor experience context. To address this knowledge gap, we report the results of a survey of visitor perceptions and behaviours related to ticks and Lyme disease risk in one of Ontario, Canada’s most highly visited protected areas. Results revealed that tick bites are the second highest perceived risk by visitors to the park, and over half (52%) reported that they limit their interactions in certain areas of the park to reduce tick and Lyme disease risk. Notably, 63% of visitors would return to Pinery if bitten by a tick. However, a high proportion of visitors noted that they would likely not return to the park if they (44%) or someone they know (40%) contracted a tick-borne disease. Furthermore, only 41% of respondents noted that they would likely return to the park if regional rates of tick-borne illnesses increased by 30% under future climate change. In light of these novel findings, we provide a number of proactive management recommendations, focused on developing or enhancing pre-visit education, communication, and outreach programmes where appropriate, that can be used by managers to effectively address this emerging human health and safety challenge in protected areas.

Operational models of the household activity-scheduling process have emerged recently. These models replicate the sequence of decisions that leads to observed patterns of human activities and travel, including which activities to conduct, with whom, for how long, at what time and location, and by what mode. Activity priority has been suggested as an important dimension in such scheduling models, particularly as a determinant for the choice and sequencing of activities. The importance of intrahousehold interactions, joint activities in particular, has led to a rapid expansion of research on this topic. However, within most scheduling models, joint activities have been addressed, at best, by assuming that they are preplanned relative to independent activities. Within this context, two important issues concerning the planning of joint activities are explored: the extent to which joint activities are preplanned and whether male and female householders share the same priority when scheduling joint activities. The data set used for the study was the 2003 Computerized Household Activity Scheduling Elicitor survey for Toronto, Ontario, Canada, which recorded information about when a particular activity was planned by respondents. In the analysis, bivariate probit models are estimated for two scheduling alternatives (impulsive or preplanned) for husband and wife. Overall, the empirical results highlight the need to move beyond static priority assumptions for determining the sequencing of activities to develop a behaviorally sound model for activity scheduling. Furthermore, differences in planning priority across individual participants should be taken into account.

Results are presented from an in-depth survey of the rescheduling decision process as it occurs in day-to-day life. The multistage survey involved a preplanning interview, followed by a prompted-recall diary supported by the Global Positioning System. Subsequent comparison of preplans with executed schedules was done to identify schedule modifications, deletions, and more-impulsive additions automatically. An open-ended interview was then used to elicit the planning time horizon of rescheduling decisions, the impetus of a decision, the impact of a single decision on the rest of the schedule, and the process that subjects go through to make a decision. Responses were then compared with different sociodemographic and activity variables by using a cross-tabulation and chi-squared analysis to determine if any associations exist. Highlights of these results include the findings that adding and deleting activities requires more talking in person, that personal need is a common cause for adding shopping activities, and that the more people involved in a decision, the longer the duration of the added activity. Three conclusions can be made from these findings. First, far more rescheduling decisions and scheduling conflicts were identified compared with previous studies. Second, there were many more varied causes of rescheduling decisions beyond those typically captured in existing rescheduling conflict models. Finally, sociodemographics had little effect on aspects of rescheduling decisions compared with activity variables. Discussion of how such results relate to past conceptual frameworks is included.

This study focuses on incorporating the travel attributes (time, cost, and availability of modes) in the activity planning time horizon analysis and presents mixed logit models estimated separately for different activity groups. Although the effects of many other variables are estimated, the focus is on the effects of location (in-home versus out-of-home) and logsum (measure of travel characteristics) variables. The inclusion of several other variables (household and individual characteristics and activity attributes) allows controlling for many factors affecting this choice and increases the explanatory power of the models. The results of the models reveal that the effects of location and travel characteristics on planning time horizon choice vary among different activity types. Toward a better understanding and quantification of the effects of location and travel attributes, the probabilities of planning the activities in different time horizons are estimated separately for in-home and out-of-home, and with an incremental increase in the logsum. Results reveal that the household obligations and active activities are very sensitive to travel characteristics; therefore, significant changes in the planning and execution of these activities may be expected with changes in the transportation system characteristics. The results of this study enhance the understanding of relationships between travel time, cost, availability of transportation modes, and the activity planning process.

Historic legal deeds are one of the most abundant resources in British archives, but also one of the most neglected. Despite the millions that survive, we know remarkably little about their manufacture, including the species of animal on which they were written. Here we present the species identification of 645 sixteenth–twentieth century skins via peptide mass fingerprinting (ZooMS), demonstrating the preferential use of sheepskin parchment. We argue that alongside their abundance and low cost, the use of sheepskins over those of other species was motivated by the increased visibility of fraudulent text erasure and modification afforded by the unique structure of their skin.

This paper attempts to model the process of activity scheduling conflict resolution with actual scheduling process data. The resolution of activity scheduling conflicts is a critical component of rule-based activity scheduling models. Many current scheduling models use an assumed priority for each activity type to estimate how activity conflicts will be resolved, but research has shown that these activity type-based priority assumptions often do not hold in actuality. Therefore, the conflict resolution data captured in the scheduling process survey were used to estimate and evaluate a number of conflict resolution models, including a decision tree model and two discrete choice models. Both the conflict resolution decision tree model and the discrete choice models showed a promising ability to predict the resolution strategies chosen almost entirely on the basis of the attributes of the activities in conflict and characteristics of the surrounding schedule. These models present a useful advance in increasing the realism and accuracy of rule-based activity scheduling models.

This paper presents results from an in-depth survey method for capturing the content and attributes of peoples' preplanned schedules. The focus is on preplanned daily activity and travel events, their typically observable attributes (event type, start and end time, location, and involved person), and the extent to which these attributes are specified and elaborated on. The survey started with an interviewer simply asking subjects to write down or discuss their schedule for the next 2 days in as much or as little detail as they knew. The interviewer subsequently probed for further details as needed. This method elicited considerable detail on decision hierarchies in the subjects' own words. Overall, it was found that activity type is the most often preplanned activity attribute, followed by location, start time, involved persons, and end time. For trips, the mode type and start time are most often planned, followed by involved persons and end time. Additional analysis further confirms that the preplanning is an ongoing decision process, wherein tentative decisions on each attribute are often made and then revisited at some point closer to execution. The implications of these findings for model development and future survey design are discussed. In particular, the results imply that activity scheduling models should adopt a nested or continuous planning loop, wherein certain activity attribute decisions are made first but are followed by subsequent stages of refinement and elaboration.

Observed travel patterns are the result of an underlying activity-scheduling process. Understanding how individuals plan and sequence their activities and trips over time will offer promise, as researchers are interested in developing models that can predict the sequences in which activities are added to the schedule during a continuous process of schedule construction. In practice, a fixed order by activity type is often assumed, but it ignores the strong interdependencies in activity and travel choices. This study presents the process of developing a mixed logit discrete choice model to predict the choice of the activity-scheduling time horizon. The model classifies activities according to the order in which they were planned (weeks or months ago, same week, same day, or impulsive). A variety of explanatory variables were used to provide a good model fit. Overall, the model demonstrates that activity planning is influenced by a wide variety of explanatory variables, including observable activity types and characteristics, spatial–temporal flexibility, and household and individual characteristics.

The development of simulation models of activity-scheduling behavior has gained momentum over the past decade as a means to forecast travel demands. Of fundamental concern in these models is the process or timing of scheduling decisions–-or planning time horizon. Conceptually, it is understood that activities are planned over varying time horizons, but little empirical evidence exists. One way to explore these issues is to ask people to self-report when they planned their activities. However, this is a difficult question for researchers to formulate and for people to comprehend and recall, because people often plan (and replan) activity attributes over an extended period of time, some without much conscious thought. The objective of this paper is to describe the development of a planning time horizon query that was part of a larger activity scheduling process survey and to provide one of the first empirical analyses based on a random sample of 373 respondents. Included is a detailed examination of activity addition, modification, and trip-planning time horizons as well as analysis of “routine” and “unrecalled” decisions. Results indicate that people have the ability to recall a high level of detail on a planning time horizon, ranging from decisions made long ago that establish an initial skeleton schedule to continued preplanning in the days leading up to the event day and impulsive decisions made the day of the event. The implications of these results for future survey design and development of an activity-scheduling process simulation model are discussed.

There is growing interest among the travel behavior community in activity scheduling and rescheduling behavior. This paper reports an analysis of activity modification behavior using extensive data for activity scheduling and rescheduling collected from a recent survey. Models that are developed describe the frequency and type of schedule modification. The analysis indicates that activity location and coscheduling with peers were much less frequently modified than activity timing and duration and that the degree of spatial flexibility of an activity plays an important role. The implications of these findings both for the modeling of scheduling and rescheduling behavior and for the development of scheduling support tools are discussed.

Observed travel patterns are the result of an underlying activity-scheduling process. Understanding how individuals plan and sequence their activities and trips over time will offer promise, as researchers are interested in developing models that can predict the sequences in which activities are added to the schedule during a continuous process of schedule construction. In practice, a fixed order by activity type is often assumed, but it ignores the strong interdependencies in activity and travel choices. This study presents the process of developing a mixed logit discrete choice model to predict the choice of the activity-scheduling time horizon. The model classifies activities according to the order in which they were planned (weeks or months ago, same week, same day, or impulsive). A variety of explanatory variables were used to provide a good model fit. Overall, the model demonstrates that activity planning is influenced by a wide variety of explanatory variables, including observable activity types and characteristics, spatial-temporal flexibility, and household and individual characteristics.

The development of simulation models of activity-scheduling behavior has gained momentum over the past decade as a means to forecast travel demands. Of fundamental concern in these models is the process or timing of scheduling decisions—or planning time horizon. Conceptually, it is understood that activities are planned over varying time horizons, but little empirical evidence exists. One way to explore these issues is to ask people to self-report when they planned their activities. However, this is a difficult question for researchers to formulate and for people to comprehend and recall, because people often plan (and replan) activity attributes over an extended period of time, some without much conscious thought. The objective of this paper is to describe the development of a planning time horizon query that was part of a larger activity scheduling process survey and to provide one of the first empirical analyses based on a random sample of 373 respondents. Included is a detailed examination of activity addition, modification, and trip-planning time horizons as well as analysis of "routine" and "unrecalled" decisions. Results indicate that people have the ability to recall a high level of detail on a planning time horizon, ranging from decisions made long ago that establish an initial skeleton schedule to continued preplanning in the days leading up to the event day and impulsive decisions made the day of the event. The implications of these results for future survey design and development of an activity-scheduling process simulation model are discussed.

The development of standards and guidance for applying quality measures in travel surveys is receiving increased attention. The most commonly reported quality measures are unit nonresponse and trip rates. How such a standard would be applied for assessing the quality of a single respondent's data remains unexplored. Furthermore, new quality indicators are needed for emerging travel survey methods, such as multiday and computerized scheduling surveys that capture observed activity travel patterns as well as information on underlying activityscheduling decision processes. A new set of rules is proposed for assessing data of individual respondents by using results from a recent survey of this type. The use of computers and a focus on decision processes led to developing several new quality indicators related to respondent login durations, data entry delays, entry timing, and recall rates. Analysis of these indicators was used to propose six rules for identifying goodquality data. A combined single rule was also developed by using principal component analysis. Further analysis of this combined indicator revealed that sociodemographic characteristics of the respondents were largely insignificant—people of all ages, genders, marital status, and employment or student status were equally able to provide good data. It also suggests that use of the combined indicator to filter out poorquality data will not necessarily introduce any additional bias into the resulting reduced sample set. The weak correlation of respondent activity or trip rates with the combined indicator implies that their usefulness for assessing data quality at an individual level is questionable.

Traditional activity-travel diary surveys have for some time served as the primary source of data for understanding and modeling travel behavior. Recent changes in policy and forecasting needs have led to the development of an emerging class of activity-scheduling process surveys that focus on the underlying behavioral mechanisms that give rise to travel and condition future change. Many of these surveys involve the use of computers for data entry over multiday periods. These changes pose new challenges and opportunities for quality assessment. At this early stage it is more important than ever to document closely the quantity and quality of data provided by such surveys as well as the associated burden and experience of respondents. This study reviews existing quality standards and seeks to develop several new data-quality measures suitable to this emerging class of surveys. Data are used from a recent household activity-scheduling survey of 271 households in Toronto, Ontario, Canada. A detailed description of the survey instrument is provided, along with an in-depth examination of key results that shed light on data quality. Included are results from a separate survey of 31 respondents concerning their experiences and perceptions of the survey. Overall, although the survey was generally successful in tracking both observed patterns and underlying decision processes over a multiday period within a household, it did come at a price in terms of respondent burden. On the basis of these results several new data-quality guidelines are suggested that incorporate use of activity-trip rates, scheduling-step rates, planning-time horizons, and log-in durations. Further specific suggestions for reducing respondent burden are suggested.

Machine-learning techniques are increasingly being applied in the areas of exploratory data analysis, prediction, and classification. At the same time that analytical techniques are expanding, new conceptual approaches to the modeling of travel are emerging in an effort to improve travel demand forecasting and better assess the impacts of emerging transportation policy. In particular, the shift toward activity-based travel analysis has led to the development of activity scheduling models. One of the key features of emerging models of this type is the attempt to simulate the order in which activities are added during a continuous process of schedule construction. In practice, a fixed order by activity type is often assumed; for example, work activities are planned first, followed by the planning of more discretionary activity types. By using observed data on the scheduling process from a small sample of households from Quebec City, Quebec, Canada, a neural network model that classifies activities according to the order in which they were planned, the planning time horizon (preplanned, planned, or impulsive), was developed. A variety of explanatory variables were used in the model related to individual-, household-, and activity-based characteristics such as spatial and temporal fixities. The model developed exhibited a relatively high degree of prediction with the test data, especially for the preplanned and impulsive categories of the planning time horizon. These results suggest that machine-learning algorithms could be used to predict the order in which activities are selected in emerging activity scheduling process models, thereby avoiding static assumptions related purely to activity type.

Forecasting the enduring and wider implications of emerging travel demand management and automobile reduction policies has proved to be a challenging task. Travel behavior researchers point to the need for more in-depth research into the underlying activity-travel scheduling processes as a means to improve the ability to do so. The objective of this research is to explore the household rescheduling and adaptation process to vehicle reduction scenarios. Descriptive results from two, small-sample, in-depth experiments are presented. The first experiment focused on households’ response to a fuel prices increase, whereas the second focused on the response of two-vehicle households to long-term removal of one vehicle from the household. Results indicate that households are aware of a broad range of possible adaptation strategies, including not only mode changes but also a wide variety of changes in activities, planning, and longer-term lifestyle changes. When people were asked to actually implement such stated strategies under realistic conditions, a much more elaborate behavioral response was elicited. This included multiple rescheduling decisions involving several activities and household members over the course of a day or even several days. Thus, even relatively straightforward stated response strategies often lead to interconnected primary and secondary effects on observed activities and travel, realized through a sequence of rescheduling decisions over time and space and across household members. These results suggest that an explicit accounting of rescheduling decision sequences in forecasting models would enhance their behavioral validity and accuracy.

Transport models and their practical applications are becoming increasingly more sophisticated, thus requiring more precise input data, including spatial data. The development, testing, and assessment of a survey method with which to collect multiday information on activity–travel patterns with a high level of spatial detail and accuracy are described. The survey consists of a computer-assisted, self-completing weekly household activity-travel diary survey program combined with an interactive map for spatial data input and visualization. Compared with traditional paper-and-pencil surveys, more accurate spatial data is gathered through this survey, especially for activity locations, routes, and trip lengths, which a first trial on a small sample of 30 households clearly demonstrated. In addition, more details on activity and travel times were collected. Fatigue effects did not become evident, and the respondent burden was considered to be acceptable by the participants. Additional testing of the respondents’ map-handling abilities showed that people have different map-orientation aptitudes that depend on sociodemographic characteristics such as gender, income, and education. This suggests that maps incorporated in travel surveys should be adapted in different ways to accommodate different levels of map handling. Future household travel surveys, especially emerging computerized and Global Positioning System-supported methods, would appear to benefit from the integration of a spatial interface both to add detail and to reduce respondent burden. This is particularly important for activity-based or activity-scheduling surveys, which increasingly attempt to obtain higher amounts of precise and detailed information on individual behavior and choices.

The author offers insights on the significant cyber defense responsibilities of the U.S. private sector. Topics discussed include the development and implementation of initiatives that would enhance sharing of cybersecurity-related data between public and private sectors, the need for private firms to maintain risk management systems that would protect them against data breaches and cyberattacks, and factors which made retailer Target vulnerable to cyberattacks.