YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories

Modeling and predicting human mobility trajectories in urban areas is an essential task for various applications including transportation modeling, disaster management, and urban planning. The recent availability of large-scale human movement data collected from mobile devices has enabled the development of complex human mobility prediction models. However, human mobility prediction methods are often trained and tested on different datasets, due to the lack of open-source large-scale human mobility datasets amid privacy concerns, posing a challenge towards conducting transparent performance comparisons between methods. To this end, we created an open-source, anonymized, metropolitan scale, and longitudinal (75 days) dataset of 100,000 individuals' human mobility trajectories, using mobile phone location data provided by Yahoo Japan Corporation (currently renamed to LY Corporation), named YJMob100K. The location pings are spatially and temporally discretized, and the metropolitan area is undisclosed to protect users' privacy. The 90-day period is composed of 75 days of business-as-usual and 15 days during an emergency, to test human mobility predictability during both normal and anomalous situations.

Effect of mobile food environments on fast food visits

Poor diets are a leading cause of morbidity and mortality. Exposure to low-quality food environments saturated with fast food outlets is hypothesized to negatively impact diet. However, food environment research has predominantly focused on static food environments around home neighborhoods and generated mixed findings. In this work, we leverage population-scale mobility data in the U.S. to examine 62M people's visits to food outlets and evaluate how food choice is influenced by the food environments people are exposed to as they move through their daily routines. We find that a 10% increase in exposure to fast food outlets in mobile environments increases individuals' odds of visitation by 20%. Using our results, we simulate multiple policy strategies for intervening on food environments to reduce fast-food outlet visits. This analysis suggests that optimal interventions are informed by spatial, temporal, and behavioral features and could have 2x to 4x larger effect than traditional interventions focused on home food environments.

The unequal effects of the health-economy trade-off during the COVID-19 pandemic

Despite the global impact of the coronavirus disease 2019 pandemic, the question of whether mandated interventions have similar economic and public health effects as spontaneous behavioural change remains unresolved. Addressing this question, and understanding differential effects across socioeconomic groups, requires building quantitative and fine-grained mechanistic models. Here we introduce a data-driven, granular, agent-based model that simulates epidemic and economic outcomes across industries, occupations and income levels. We validate the model by reproducing key outcomes of the first wave of coronavirus disease 2019 in the New York metropolitan area. The key mechanism coupling the epidemic and economic modules is the reduction in consumption due to fear of infection. In counterfactual experiments, we show that a similar trade-off between epidemic and economic outcomes exists both when individuals change their behaviour due to fear of infection and when non-pharmaceutical interventions are imposed. Low-income workers, who perform in-person occupations in customer-facing industries, face the strongest trade-off.

Temporal clustering of social interactions trades-off disease spreading and knowledge diffusion

Non-pharmaceutical measures such as preventive quarantines, remote working, school and workplace closures, lockdowns, etc. have shown effectiveness from an epidemic control perspective; however, they have also significant negative consequences on social life and relationships, work routines and community engagement. In particular, complex ideas, work and school collaborations, innovative discoveries and resilient norms formation and maintenance, which often require face-to-face interactions of two or more parties to be developed and synergically coordinated, are particularly affected. In this study, we propose an alternative hybrid solution that balances the slowdown of epidemic diffusion with the preservation of face-to-face interactions, that we test simulating a disease and a knowledge spreading simultaneously on a network of contacts. Our approach involves a two-step partitioning of the population. First, we tune the level of node clustering, creating 'social bubbles' with increased contacts within each bubble and fewer outside, while maintaining the average number of contacts in each network. Second, we tune the level of temporal clustering by pairing, for a certain time interval, nodes from specific social bubbles. Our results demonstrate that a hybrid approach can achieve better trade-offs between epidemic control and complex knowledge diffusion. The versatility of our model enables tuning and refining clustering levels to optimally achieve the desired trade-off, based on the potentially changing characteristics of a disease or knowledge diffusion process.

Population mobility data provides meaningful indicators of fast food intake and diet-related diseases in diverse populations

The characteristics of food environments people are exposed to, such as the density of fast food (FF) outlets, can impact their diet and risk for diet-related chronic disease. Previous studies examining the relationship between food environments and nutritional health have produced mixed findings, potentially due to the predominant focus on static food environments around people's homes. As smartphone ownership increases, large-scale data on human mobility (i.e., smartphone geolocations) represents a promising resource for studying dynamic food environments that people have access to and visit as they move throughout their day. This study investigates whether mobility data provides meaningful indicators of diet, measured as FF intake, and diet-related disease, evaluating its usefulness for food environment research. Using a mobility dataset consisting of 14.5 million visits to geolocated food outlets in Los Angeles County (LAC) across a representative sample of 243,644 anonymous and opted-in adult smartphone users in LAC, we construct measures of visits to FF outlets aggregated over users living in neighborhood. We find that the aggregated measures strongly and significantly correspond to self-reported FF intake, obesity, and diabetes in a diverse, representative sample of 8,036 LAC adults included in a population health survey carried out by the LAC Department of Public Health. Visits to FF outlets were a better predictor of individuals' obesity and diabetes than their self-reported FF intake, controlling for other known risks. These findings suggest mobility data represents a valid tool to study people's use of dynamic food environments and links to diet and health.

Home-to-school pedestrian mobility GPS data from a citizen science experiment in the Barcelona area

The analysis of pedestrian GPS datasets is fundamental to further advance on the study and the design of walkable cities. The highest resolution GPS data can characterize micro-mobility patterns and pedestrians' micro-motives in relation to a small-scale urban context. Purposed-based recurrent mobility data inside people's neighbourhoods is an important source in these sorts of studies. However, micro-mobility around people's homes is generally unavailable, and if data exists, it is generally not shareable often due to privacy issues. Citizen science and its public involvement practices in scientific research are valid options to circumvent these challenges and provide meaningful datasets for walkable cities. The study presents GPS records from single-day home-to-school pedestrian mobility of 10 schools in the Barcelona Metropolitan area (Spain). The research provides pedestrian mobility from an age-homogeneous group of people. The study shares processed records with specific filtering, cleaning, and interpolation procedures that can facilitate and accelerate data usage. Citizen science practices during the whole research process are reported to offer a complete perspective of the data collected.

Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics

Urbanization and its problems require an in-depth and comprehensive understanding of urban dynamics, especially the complex and diversified lifestyles in modern cities. Digitally acquired data can accurately capture complex human activity, but it lacks the interpretability of demographic data. In this paper, we study a privacy-enhanced dataset of the mobility visitation patterns of 1.2 million people to 1.1 million places in 11 metro areas in the U.S. to detect the latent mobility behaviors and lifestyles in the largest American cities. Despite the considerable complexity of mobility visitations, we found that lifestyles can be automatically decomposed into only 12 latent interpretable activity behaviors on how people combine shopping, eating, working, or using their free time. Rather than describing individuals with a single lifestyle, we find that city dwellers' behavior is a mixture of those behaviors. Those detected latent activity behaviors are equally present across cities and cannot be fully explained by main demographic features. Finally, we find those latent behaviors are associated with dynamics like experienced income segregation, transportation, or healthy behaviors in cities, even after controlling for demographic features. Our results signal the importance of complementing traditional census data with activity behaviors to understand urban dynamics.

Supplementary Information

The online version contains supplementary material available at 10.1140/epjds/s13688-023-00390-w.

Behavioral changes during the COVID-19 pandemic decreased income diversity of urban encounters

Diversity of physical encounters in urban environments is known to spur economic productivity while also fostering social capital. However, mobility restrictions during the pandemic have forced people to reduce urban encounters, raising questions about the social implications of behavioral changes. In this paper, we study how individual income diversity of urban encounters changed during the pandemic, using a large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in Boston, Dallas, Los Angeles, and Seattle, across three years spanning before and during the pandemic. We find that the diversity of urban encounters has substantially decreased (by 15% to 30%) during the pandemic and has persisted through late 2021, even though aggregated mobility metrics have recovered to pre-pandemic levels. Counterfactual analyses show that behavioral changes including lower willingness to explore new places further decreased the diversity of encounters in the long term. Our findings provide implications for managing the trade-off between the stringency of COVID-19 policies and the diversity of urban encounters as we move beyond the pandemic.

Diversity beyond density: experienced social mixing of urban streets

Urban density, in the form of residents’ and visitors’ concentration, is long considered to foster diverse exchanges of interpersonal knowledge and skills, which are intrinsic to sustainable human settlements. However, with current urban studies primarily devoted to city and district-level analysis, we cannot unveil the elemental connection between urban density and diversity. Here we use an anonymized and privacy-enhanced mobile data set of 0.5 million opted-in users from three metropolitan areas in the U.S. to show that at the scale of urban streets, density is not the only path to diversity. We represent the diversity of each street with the Experienced Social Mixing (ESM), which describes the chances of people meeting diverse income groups throughout their daily experience. We conduct multiple experiments and show that the concentration of visitors only explains 26% of street-level ESM. However, adjacent amenities, residential diversity, and income level account for 44% of the ESM. Moreover, using longitudinal business data, we show that streets with an increased number of food businesses have seen an increased ESM from 2016 to 2018. Lastly, although streets with more visitors are more likely to have crime, diverse streets tend to have fewer crimes. These findings suggest that cities can leverage many tools beyond density to curate a diverse and safe street experience for people.

Access to health literacy best practices and the role of health libraries (BiblioSUS Network BVS) in Brazil

Introduction

Health Literacy (HL) has received recognition of its role and is proposed as a key element of incrementing wellbeing in public health. In the south, this discussion is engaged but a dialogue north-south needs to be incremented to allow further comprehension and implementation of HL best practices. The BiblioSUS Network in Brazil engaged in this discussion while it aims to expand and democratize access to health promotion & HL best practices, disseminated through the virtual health libraries (BVS). As a distribution model of content production by the Ministry of Health in Brazil it reaches a large audience in the country.

Methods

The target of this research includes BiblioSUS workers, representing the most diverse areas of knowledge and the community served by the Network. Data collection included an online survey on HL using the HLS-EU-BR instrument.

Results

The study involved 717 members of the Network and community users of cooperating libraries. Inadequate (12%) and problematic (36%) HL levels revealed that 48% had low levels of HL. The analysis of the 12 variables to assess community's health information needs, showed that 83% of participants do not use the network for health promotion needs, 70.4% do not use for quality of life purposes, but that 41.4% use it for related diseases issues.

Discussion

This research is the first of its kind and emphasize the need to promote health promotion and HL by several means (e.g. like BiblioSUS Network libraries). Communities need to be reached out to help them make decisions favorable to health in everyday life and settings (e.g. home, community, work). While health promotion and disease prevention are targeted, the Brazilian population need to have easier access to reliable health promotion information, in order to contribute to citizens’ empowerment in health.

Rhythm of the streets: a street classification framework based on street activity patterns

As the living tissue connecting urban places, streets play significant roles in driving city development, providing essential access, and promoting human interactions. Understanding street activities and how these activities vary across different streets is critical for designing both efficient and livable streets. However, current street classification frameworks primarily focus on either streets' functions in transportation networks or their adjacent land uses rather than actual activity patterns, resulting in coarse classifications. This research proposes an activity-based street classification framework to categorize street segments based on their temporal street activity patterns, which is derived from high-resolution de-identified and privacy-enhanced mobility data. We then apply the proposed framework to 18,023 street segments in the City of Boston and reveal 10 distinct activity-based street types (ASTs). These ASTs highlight dynamic street activities on streets, which complements existing street classification frameworks, which focus on the static or transportation characteristics of the street segments. Our results show that a street classification framework based on temporal street activity patterns can identify street categories at a finer granularity than current methods, which can offer useful implications for state-of-the-art urban management and planning. In particular, we find that our classification distinguishes better those streets where crime is more prevalent than current functional or contextual classifications of streets.

Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas

Detailed characterization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission across different settings can help design less disruptive interventions. We used real-time, privacy-enhanced mobility data in the New York City, NY and Seattle, WA metropolitan areas to build a detailed agent-based model of SARS-CoV-2 infection to estimate the where, when, and magnitude of transmission events during the pandemic's first wave. We estimate that only 18% of individuals produce most infections (80%), with about 10% of events that can be considered superspreading events (SSEs). Although mass gatherings present an important risk for SSEs, we estimate that the bulk of transmission occurred in smaller events in settings like workplaces, grocery stores, or food venues. The places most important for transmission change during the pandemic and are different across cities, signaling the large underlying behavioral component underneath them. Our modeling complements case studies and epidemiological data and indicates that real-time tracking of transmission events could help evaluate and define targeted mitigation policies.

News or social media? Socio-economic divide of mobile service consumption

Reliable and timely information on socio-economic status and divides is critical to social and economic research and policing. Novel data sources from mobile communication platforms have enabled new cost-effective approaches and models to investigate social disparity, but their lack of interpretability, accuracy or scale has limited their relevance to date. We investigate the divide in digital mobile service usage with a large dataset of 3.7 billion time-stamped and geo-referenced mobile traffic records in a major European country, and find profound geographical unevenness in mobile service usage-especially on news, e-mail, social media consumption and audio/video streaming. We relate such diversity with income, educational attainment and inequality, and reveal how low-income or low-education areas are more likely to engage in video streaming or social media and less in news consumption, information searching, e-mail or audio streaming. The digital usage gap is so large that we can accurately infer the socio-economic status of a small area or even its Gini coefficient only from aggregated data traffic. Our results make the case for an inexpensive, privacy-preserving, real-time and scalable way to understand the digital usage divide and, in turn, poverty, unemployment or economic growth in our societies through mobile phone data.

Mobility patterns are associated with experienced income segregation in large US cities

Traditional understanding of urban income segregation is largely based on static coarse-grained residential patterns. However, these do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Using a large-scale high-resolution mobility data set of 4.5 million mobile phone users and 1.1 million places in 11 large American cities, we show that income segregation experienced in places and by individuals can differ greatly even within close spatial proximity. To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual’s tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). Interestingly, while the latter is more strongly associated with demographic characteristics, the former is more strongly associated with mobility behavioral variables. Our results suggest that mobility behavior plays an important role in experienced income segregation of individuals. To measure this form of income segregation, urban researchers should take into account mobility behavior and not only residential patterns.

Urban income segregation is often discussed in terms of where people live. Here, the authors show that the way people experience income segregation is also associated with their mobility patterns and the places they visit.

Effect of COVID-19 response policies on walking behavior in US cities

The COVID-19 pandemic is causing mass disruption to our daily lives. We integrate mobility data from mobile devices and area-level data to study the walking patterns of 1.62 million anonymous users in 10 metropolitan areas in the United States. The data covers the period from mid-February 2020 (pre-lockdown) to late June 2020 (easing of lockdown restrictions). We detect when users were walking, distance walked and time of the walk, and classify each walk as recreational or utilitarian. Our results reveal dramatic declines in walking, particularly utilitarian walking, while recreational walking has recovered and even surpassed pre-pandemic levels. Our findings also demonstrate important social patterns, widening existing inequalities in walking behavior. COVID-19 response measures have a larger impact on walking behavior for those from low-income areas and high use of public transportation. Provision of equal opportunities to support walking is key to opening up our society and economy.

Housing Prices and the Skills Composition of Neighborhoods

In the United States (US), low-income workers are being pushed away from city centers where the cost of living is high. The effects of such changes on labor mobility and housing price have been explored in the literature. However, few studies have focused on the occupations and specific skills that identify the most susceptible workers. For example, it has become increasingly challenging to fill the service sector jobs in the San Francisco (SF) Bay Area because appropriately skilled workers cannot afford the growing cost of living within commuting distance. With this example in mind, how does a neighborhood's skill composition change as a result of higher housing prices? Are there certain skill sets that are being pushed to the geographical periphery of a city despite their essentialness to the city's economy? Our study focuses on the impact of housing prices with a granular view of skills compositions to answer the following question: Has the density of cognitive skill workers been increasing in a gentrified area? We hypothesize that, over time, low-skilled workers are pushed away from downtown or areas where high-skill establishments thrive. Our preliminary results show that high-level cognitive skills are getting closer to the city center indicating adaptation to the increase of median housing prices as opposed to low-level physical skills that got further away. We examined tracts that the literature indicates as gentrified areas and found a pattern in which there is a temporal increase in median housing prices and the number of business establishments coupled with an increase in the percentage of skilled cognitive workers.

Universal resilience patterns in labor markets

Cities are the innovation centers of the US economy, but technological disruptions can exclude workers and inhibit a middle class. Therefore, urban policy must promote the jobs and skills that increase worker pay, create employment, and foster economic resilience. In this paper, we model labor market resilience with an ecologically-inspired job network constructed from the similarity of occupations’ skill requirements. This framework reveals that the economic resilience of cities is universally and uniquely determined by the connectivity within a city’s job network. US cities with greater job connectivity experienced lower unemployment during the Great Recession. Further, cities that increase their job connectivity see increasing wage bills, and workers of embedded occupations enjoy higher wages than their peers elsewhere. Finally, we show how job connectivity may clarify the augmenting and deleterious impact of automation in US cities. Policies that promote labor connectivity may grow labor markets and promote economic resilience.

Recent technological, social, and educational changes are profoundly impacting our work, but what makes labour markets resilient to those labour shocks? Here, the authors show that labour markets resemble ecological systems whose resilience depends critically on the network of skill similarities between different jobs.

EAN consensus statement for management of patients with neurological diseases during the COVID-19 pandemic

BACKGROUND AND PURPOSE

The recent SARS-CoV-2 pandemic has posed multiple challenges to the practice of clinical neurology including recognition of emerging neurological complications and management of coexistent neurological diseases. In a fast-evolving pandemic, evidence-based studies are lacking in many areas. This paper presents European Academy of Neurology (EAN) expert consensus statements to guide neurologists caring for patients with COVID-19.

METHODS

A refined Delphi methodology was applied. In round 1, statements were provided by EAN scientific panels (SPs). In round 2, these statements were circulated to SP members not involved in writing them, asking for agreement/disagreement. Items with agreement >70% were retained for round 3, in which SP co-chairs rated importance on a five-point Likert scale. Results were graded by importance and reported as consensus statements.

RESULTS

In round one, 70 statements were provided by 23 SPs. In round two, 259/1061 SP member responses were received. Fifty-nine statements obtained >70% agreement and were retained. In round three, responses were received from 55 co-chairs of 29 SPs. Whilst general recommendations related to prevention of COVID-19 transmission had high levels of agreement and importance, opinion was more varied concerning statements related to therapy.

CONCLUSION

This is the first structured consensus statement on good clinical practice in patients with neurological disease during the COVID-19 pandemic that provides immediate guidance for neurologists. In this fast-evolving pandemic, a rapid response using refined Delphi methodology is possible, but guidance may be subject to change as further evidence emerges.

Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19

While severe social-distancing measures have proven effective in slowing the coronavirus disease 2019 (COVID-19) pandemic, second-wave scenarios are likely to emerge as restrictions are lifted. Here we integrate anonymized, geolocalized mobility data with census and demographic data to build a detailed agent-based model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in the Boston metropolitan area. We find that a period of strict social distancing followed by a robust level of testing, contact-tracing and household quarantine could keep the disease within the capacity of the healthcare system while enabling the reopening of economic activities. Our results show that a response system based on enhanced testing and contact tracing can have a major role in relaxing social-distancing interventions in the absence of herd immunity against SARS-CoV-2.

The international European Academy of Neurology survey on neurological symptoms in patients with COVID-19 infection

Background and purpose

Although the main clinical features of COVID‐19 infection are pulmonary, several associated neurological signs, symptoms and diseases are emerging. The incidence and characteristics of neurological complications are unclear. For this reason, the European Academy of Neurology (EAN) core COVID‐19 Task Force initiated a survey on neurological symptoms observed in patients with COVID‐19 infection.

Methods

A 17‐question online survey was made available on the EAN website and distributed to EAN members and other worldwide physicians starting on 9 April 2020.

Results

By 27 April 2020, proper data were collected from 2343 responders (out of 4199), of whom 82.0% were neurologists, mostly from Europe. Most responders (74.7%) consulted patients with COVID‐19 mainly in emergency rooms and in COVID‐19 units. The majority (67.0%) had evaluated fewer than 10 patients with neurological manifestations of COVID‐19 (neuro COVID‐19). The most frequently reported neurological findings were headache (61.9%), myalgia (50.4%), anosmia (49.2%), ageusia (39.8%), impaired consciousness (29.3%) and psychomotor agitation (26.7%). Encephalopathy and acute cerebrovascular disorders were reported at 21.0%. Neurological manifestations were generally interpreted as being possibly related to COVID‐19; they were most commonly recognized in patients with multiple general symptoms and occurred at any time during infection.

Conclusion

Neurologists are currently and actively involved in the management of neurological issues related to the COVID‐19 pandemic. This survey justifies setting up a prospective registry to better capture the prevalence of patients with neuro COVID‐19, neurological disease characteristics and the contribution of neurological manifestations to outcome.

Modeling the impact of social distancing, testing, contact tracing and household quarantine on second-wave scenarios of the COVID-19 epidemic

The new coronavirus disease 2019 (COVID-19) has required the implementation of severe mobility restrictions and social distancing measures worldwide. While these measures have been proven effective in abating the epidemic in several countries, it is important to estimate the effectiveness of testing and tracing strategies to avoid a potential second wave of the COVID-19 epidemic. We integrate highly detailed (anonymized, privacy-enhanced) mobility data from mobile devices, with census and demographic data to build a detailed agent-based model to describe the transmission dynamics of SARS-CoV-2 in the Boston metropolitan area. We find that enforcing strict social distancing followed by a policy based on a robust level of testing, contact-tracing and household quarantine, could keep the disease at a level that does not exceed the capacity of the health care system. Assuming the identification of 50% of the symptomatic infections, and the tracing of 40% of their contacts and households, which corresponds to about 9% of individuals quarantined, the ensuing reduction in transmission allows the reopening of economic activities while attaining a manageable impact on the health care system. Our results show that a response system based on enhanced testing and contact tracing can play a major role in relaxing social distancing interventions in the absence of herd immunity against SARS-CoV-2.

Weather impacts expressed sentiment

We conduct the largest ever investigation into the relationship between meteorological conditions and the sentiment of human expressions. To do this, we employ over three and a half billion social media posts from tens of millions of individuals from both Facebook and Twitter between 2009 and 2016. We find that cold temperatures, hot temperatures, precipitation, narrower daily temperature ranges, humidity, and cloud cover are all associated with worsened expressions of sentiment, even when excluding weather-related posts. We compare the magnitude of our estimates with the effect sizes associated with notable historical events occurring within our data.

Efficacy of pallidal stimulation in isolated dystonia: a systematic review and meta-analysis

The aim of this review was to provide strong clinical evidence of the efficacy of deep brain stimulation (DBS) of the globus pallidus internus (GPi) in isolated inherited or idiopathic dystonia. Eligible studies were identified after a systematic literature review of the effects of bilateral GPi‐DBS in isolated dystonia. Absolute and percentage changes from baseline in the Burke–Fahn–Marsden Dystonia Rating Scale (BFMDRS) motor and disability scores were pooled, and associations between treatment effect and patient characteristics were explored using meta‐regression. In total, 24 studies were included in the meta‐analysis, comprising 523 patients. The mean absolute and percentage improvements in BFMDRS motor score at the last follow‐up (mean 32.5 months; 24 studies) were 26.6 points [95% confidence interval (CI), 22.4–30.8] and 65.2% (95% CI, 59.6–70.7), respectively. The corresponding changes in disability score at the last follow‐up (mean 32.9 months; 14 studies) were 6.4 points (95% CI, 5.0–7.8) and 58.6% (95% CI, 50.3–66.9). Multivariate meta‐regression of absolute scores indicated that higher BFMDRS motor and disability scores before surgery, together with younger age at time of surgery, were the main factors associated with significantly better DBS outcomes at the latest follow‐up. Reporting of safety data was frequently inconsistent and could not be included in the meta‐analysis. In conclusion, patients with isolated inherited or idiopathic dystonia significantly improved after GPi‐DBS. Better outcomes were associated with greater dystonia severity at baseline. These findings should be taken into consideration for improving patient selection for DBS.

Delivering a sustainable trauma management training programme tailored for low-resource settings in East, Central and Southern African countries using a cascading course model

BACKGROUND

Injuries cause five million deaths and 279 Disability Adjusted Life Years (DALYS) each year worldwide. The COSECSA Oxford Orthopaedic Link (COOL) is a multi-country partnership programme that has delivered training in trauma management to nine sub-Saharan countries across a wide-cadre of health-workers using a model of "primary" courses delivered by UK instructors, followed by "cascading" courses led by local faculty. This study examines the impact on knowledge and clinical confidence among health-workers, and compares the performance of "cascading" and "primary" courses delivered in low-resource settings.

METHODS

Data was collated from 1030 candidates (119 Clinical Officers, 540 Doctors, 260 Nurses and 111 Medical Students) trained over 28 courses (9 "primary" and 19 "cascading" courses) in nine sub-Saharan countries between 2012 and 2013. Knowledge and clinical confidence of candidates were assessed using pre- and post-course MCQs and confidence matrix rating of clinical scenarios. Changes were measured in relation to co-variants of gender, job roles and primary versus cascading courses. Multivariate regression modelling and cost analysis was performed to examine the impact of primary versus cascading courses on candidates' performance.

FINDINGS

There was a significant improvement in knowledge (58% to 77%, p<0.05) and clinical confidence (68% to 90%, p<0.05) post-course. "Non-doctors" demonstrated a greater improvement in knowledge (22%) and confidence (24%) following the course (p<0.05). The degree of improvement of MCQ scores differed significantly, with the cascading courses (21%) outperforming primary courses (15%) (p<0.002). This is further supported by multivariate regression modelling where cascading courses are a strong predictor for improvement in MCQ scores (Coef=4.83, p<0.05).

INTERPRETATION

Trauma management training of health-workers plays a pivotal role in tackling the ever-growing trauma burden in Africa. Our study suggests cascading PTC courses may be an effective model in delivering trauma training in low-resource settings, however further studies are required to determine its efficacy in improving clinical competence and retention of knowledge and skills in the long term.

Rapid assessment of disaster damage using social media activity

Could social media data aid in disaster response and damage assessment? Countries face both an increasing frequency and an increasing intensity of natural disasters resulting from climate change. During such events, citizens turn to social media platforms for disaster-related communication and information. Social media improves situational awareness, facilitates dissemination of emergency information, enables early warning systems, and helps coordinate relief efforts. In addition, the spatiotemporal distribution of disaster-related messages helps with the real-time monitoring and assessment of the disaster itself. We present a multiscale analysis of Twitter activity before, during, and after Hurricane Sandy. We examine the online response of 50 metropolitan areas of the United States and find a strong relationship between proximity to Sandy's path and hurricane-related social media activity. We show that real and perceived threats, together with physical disaster effects, are directly observable through the intensity and composition of Twitter's message stream. We demonstrate that per-capita Twitter activity strongly correlates with the per-capita economic damage inflicted by the hurricane. We verify our findings for a wide range of disasters and suggest that massive online social networks can be used for rapid assessment of damage caused by a large-scale disaster.

Fully nonlinear dynamics of stochastic thin-film dewetting

The spontaneous formation of droplets via dewetting of a thin fluid film from a solid substrate allows materials nanostructuring. Often, it is crucial to be able to control the evolution, and to produce patterns characterized by regularly spaced droplets. While thermal fluctuations are expected to play a role in the dewetting process, their relevance has remained poorly understood, particularly during the nonlinear stages of evolution that involve droplet formation. Within a stochastic lubrication framework, we show that thermal noise substantially influences the process of droplets formation. Stochastic systems feature a smaller number of droplets with a larger variability in size and space distribution, when compared to their deterministic counterparts. Finally, we discuss the influence of stochasticity on droplet coarsening for asymptotically long times.

Daily Rhythms in Mobile Telephone Communication

Circadian rhythms are known to be important drivers of human activity and the recent availability of electronic records of human behaviour has provided fine-grained data of temporal patterns of activity on a large scale. Further, questionnaire studies have identified important individual differences in circadian rhythms, with people broadly categorised into morning-like or evening-like individuals. However, little is known about the social aspects of these circadian rhythms, or how they vary across individuals. In this study we use a unique 18-month dataset that combines mobile phone calls and questionnaire data to examine individual differences in the daily rhythms of mobile phone activity. We demonstrate clear individual differences in daily patterns of phone calls, and show that these individual differences are persistent despite a high degree of turnover in the individuals’ social networks. Further, women’s calls were longer than men’s calls, especially during the evening and at night, and these calls were typically focused on a small number of emotionally intense relationships. These results demonstrate that individual differences in circadian rhythms are not just related to broad patterns of morningness and eveningness, but have a strong social component, in directing phone calls to specific individuals at specific times of day.

Social media fingerprints of unemployment

Recent widespread adoption of electronic and pervasive technologies has enabled the study of human behavior at an unprecedented level, uncovering universal patterns underlying human activity, mobility, and interpersonal communication. In the present work, we investigate whether deviations from these universal patterns may reveal information about the socio-economical status of geographical regions. We quantify the extent to which deviations in diurnal rhythm, mobility patterns, and communication styles across regions relate to their unemployment incidence. For this we examine a country-scale publicly articulated social media dataset, where we quantify individual behavioral features from over 19 million geo-located messages distributed among more than 340 different Spanish economic regions, inferred by computing communities of cohesive mobility fluxes. We find that regions exhibiting more diverse mobility fluxes, earlier diurnal rhythms, and more correct grammatical styles display lower unemployment rates. As a result, we provide a simple model able to produce accurate, easily interpretable reconstruction of regional unemployment incidence from their social-media digital fingerprints alone. Our results show that cost-effective economical indicators can be built based on publicly-available social media datasets.

Performance of social network sensors during Hurricane Sandy

Information flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow and derive early-warning sensors, thus improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioral properties derived from the "friendship paradox", is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in users' network centrality effectively translate into moderate awareness advantage (up to 26 hours); and that geo-location of users within or outside of the hurricane-affected area plays a significant role in determining the scale of such an advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility to implement a simple "sentiment sensing" technique that can detect and locate disasters.

Subjectively perceived personality and mood changes associated with subthalamic stimulation in patients with Parkinson's disease

BACKGROUND

Clinical and ethical implications of personality and mood changes in Parkinson's disease (PD) patients treated with subthalamic deep brain stimulation (STN-DBS) are under debate. Although subjectively perceived personality changes are often mentioned by patients and caregivers, few empirical studies concerning these changes exist. Therefore, we analysed subjectively perceived personality and mood changes in STN-DBS PD patients.

METHOD

In this prospective study of the ELSA-DBS group, 27 PD patients were assessed preoperatively and 1 year after STN-DBS surgery. Two categories, personality and mood changes, were analysed with semi-structured interviews. Patients were grouped into personality change yes/no, as well as positive/negative mood change groups. Caregivers were additionally interviewed about patients' personality changes. Characteristics of each group were assessed with standard neurological and psychiatric measurements. Predictors for changes were analysed.

RESULTS

Personality changes were perceived by six of 27 (22%) patients and by 10 of 23 caregivers (44%). The preoperative hypomania trait was a significant predictor for personality change perceived by patients. Of 21 patients, 12 (57%) perceived mood as positively changed. Higher apathy and anxiety ratings were found in the negative change group.

CONCLUSIONS

Our results show that a high proportion of PD patients and caregivers perceived personality changes under STN-DBS, emphasizing the relevance of this topic. Mood changed in positive and negative directions. Standard measurement scales failed to adequately reflect personality or mood changes subjectively perceived by patients. A more individualized preoperative screening and preparation for patients and caregivers, as well as postoperative support, could therefore be useful.

Pallidal Deep Brain Stimulation in Cervical Dystonia: Clinical Outcome in Four Cases

Objective:

Report on the clinical results following bilateral globus pallidus interna deep brain stimulation in four patients (one female and three males) with severe cervical dystonia, mean age 48 years (range 37-67).

Methods:

All four patients had failed extensive medical and botulinum toxin treatment. The mean duration of the disease was nine years (range 4-15 years). Patients were assessed pre and postoperatively using the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS). Preoperatively, the mean TWSTRS total score was 43.2 (range 28-60.5). Posteroventral pallidal deep brain stimulators were inserted using MRI and microelectrode recording guidance. Last follow-up was 15 months for the four patients.

Results:

Mean reduction in the TWSTRS total scores at last follow- up was 73% (range 61- 85%). Improvement in pain occurred soon after deep brain stimulation surgery. Motor improvement was delayed and prolonged over several months. Frequent adjustment in the stimulation parameters was necessary in the first three months.

Conclusion:

Bilateral pallidal stimulation is effective in management of selected cases of intractable cervical dystonia.

Macroscopic response to microscopic intrinsic noise in three-dimensional Fisher fronts

We study the dynamics of three-dimensional Fisher fronts in the presence of density fluctuations. To this end we simulate the Fisher equation subject to stochastic internal noise, and study how the front moves and roughens as a function of the number of particles in the system, N. Our results suggest that the macroscopic behavior of the system is driven by the microscopic dynamics at its leading edge where number fluctuations are dominated by rare events. Contrary to naive expectations, the strength of front fluctuations decays extremely slowly as 1/logN, inducing large-scale fluctuations which we find belong to the one-dimensional Kardar-Parisi-Zhang universality class of kinetically rough interfaces. Hence, we find that there is no weak-noise regime for Fisher fronts, even for realistic numbers of particles in macroscopic systems.

Limited communication capacity unveils strategies for human interaction

Connectivity is the key process that characterizes the structural and functional properties of social networks. However, the bursty activity of dyadic interactions may hinder the discrimination of inactive ties from large interevent times in active ones. We develop a principled method to detect tie de-activation and apply it to a large longitudinal, cross-sectional communication dataset (≈19 months, ≈20 million people). Contrary to the perception of ever-growing connectivity, we observe that individuals exhibit a finite communication capacity, which limits the number of ties they can maintain active in time. On average men display higher capacity than women, and this capacity decreases for both genders over their lifespan. Separating communication capacity from activity reveals a diverse range of tie activation strategies, from stable to exploratory. This allows us to draw novel relationships between individual strategies for human interaction and the evolution of social networks at global scale.

The predictability of consumer visitation patterns

We consider hundreds of thousands of individual economic transactions to ask: how predictable are consumers in their merchant visitation patterns? Our results suggest that, in the long-run, much of our seemingly elective activity is actually highly predictable. Notwithstanding a wide range of individual preferences, shoppers share regularities in how they visit merchant locations over time. Yet while aggregate behavior is largely predictable, the interleaving of shopping events introduces important stochastic elements at short time scales. These short- and long-scale patterns suggest a theoretical upper bound on predictability, and describe the accuracy of a Markov model in predicting a person's next location. We incorporate population-level transition probabilities in the predictive models, and find that in many cases these improve accuracy. While our results point to the elusiveness of precise predictions about where a person will go next, they suggest the existence, at large time-scales, of regularities across the population.

Wnt activation promotes neuronal differentiation of glioblastoma

One of the biggest challenges in tumour research is the possibility to reprogram cancer cells towards less aggressive phenotypes. In this study, we reprogrammed primary Glioblastoma multiforme (GBM)-derived cells towards a more differentiated and less oncogenic phenotype by activating the Wnt pathway in a hypoxic microenvironment. Hypoxia usually correlates with malignant behaviours in cancer cells, but it has been recently involved, together with Wnt signalling, in the differentiation of embryonic and neural stem cells. Here, we demonstrate that treatment with Wnt ligands, or overexpression of β-catenin, mediate neuronal differentiation and halt proliferation in primary GBM cells. An hypoxic environment cooperates with Wnt-induced differentiation, in line with our finding that hypoxia inducible factor-1α (HIF-1α) is instrumental and required to sustain the expression of β-catenin transcriptional partners TCF-1 and LEF-1. In addition, we also found that Wnt-induced GBM cell differentiation inhibits Notch signalling, and thus gain of Wnt and loss of Notch cooperate in the activation of a pro-neuronal differentiation program. Intriguingly, the GBM sub-population enriched of cancer stem cells (CD133⁺ fraction) is the primary target of the pro-differentiating effects mediated by the crosstalk between HIF-1α, Wnt, and Notch signalling. By using zebrafish transgenics and mutants as model systems to visualize and manipulate in vivo the Wnt pathway, we confirm that Wnt pathway activation is able to promote neuronal differentiation and inhibit Notch signalling of primary human GBM cells also in this in vivo set-up. In conclusion, these findings shed light on an unsuspected crosstalk between hypoxia, Wnt and Notch signalling in GBM, and suggest the potential to manipulate these microenvironmental signals to blunt GBM malignancy.

Role of neuronal and inducible nitric oxide synthases in the guinea pig ileum myenteric plexus during in vitro ischemia and reperfusion

BACKGROUND

Intestinal ischemia and reperfusion (I/R) injury leads to abnormalities in motility, namely delay of transit, caused by damage to myenteric neurons. Alterations of the nitrergic transmission may occur in these conditions. This study investigated whether an in vitro I/R injury may affect nitric oxide (NO) production from the myenteric plexus of the guinea pig ileum and which NO synthase (NOS) isoform is involved.

METHODS

The distribution of the neuronal (n) and inducible (i) NOS was determined by immunohistochemistry during 60 min of glucose/oxygen deprivation (in vitro ischemia) followed by 60 min of reperfusion. The protein and mRNA levels of nNOS and iNOS were investigated by Western-immunoblotting and real time RT-PCR, respectively. NO levels were quantified as nitrite/nitrate.

KEY RESULTS

After in vitro I/R the proportion of nNOS-expressing neurons and protein levels remained unchanged. nNOS mRNA levels increased 60 min after inducing ischemia and in the following 5 min of reperfusion. iNOS-immunoreactive neurons, protein and mRNA levels were up-regulated during the whole I/R period. A significant increase of nitrite/nitrate levels was observed in the first 5 min after inducing I/R and was significantly reduced by N(ω) -propyl-l-arginine and 1400 W, selective inhibitors of nNOS and iNOS, respectively.

CONCLUSIONS & INFERENCES

Our data demonstrate that both iNOS and nNOS represent sources for NO overproduction in ileal myenteric plexus during I/R, although iNOS undergoes more consistent changes suggesting a more relevant role for this isoform in the alterations occurring in myenteric neurons following I/R.

[Work-related injuries in Monfalcone shipyard (2000-2010)]

Shipbuilding industry exhibits higher injury rates at the workplace than those reported in other industrial sectors. Work-related injuries (n = 6714) occurred from 2000 to 2010 at the shipyard of Monfalcone (Gorizia-Italy) were considered. Injury frequency incidence (IF = number of accidents/number of workers x 1000) and injury severity rate were estimated. Among permanent (directly employed) shipyard workers, the IF ranged from 294.6 in 2000 to 113.7 in 2010. Injury severity rates showed the same decreasing trend. The IF for contract workers was unchanged over the calendar period (110.5 in 2000 to 110.9 in 2010) with no significant change in the injury severity rate. These findings suggest an improvement of the working condition for permanent shipyard workers as a result of prevention policies (education and surveillance) and the automation of production processes. Further preventative interventions are suggested to minimize work-related injuries and to promote health and safety in the shipbuilding industry.

Subthalamic deep brain stimulation at individualized frequencies for Parkinson disease

OBJECTIVES

The oscillation model of Parkinson disease (PD) states that, in the subthalamic nucleus (STN), increased θ (4-10 Hz) and β (11-30 Hz) frequencies were associated with worsening whereas γ frequencies (31-100 Hz) were associated with improvement of motor symptoms. However, the peak STN frequency in each band varied widely from subject to subject. We hypothesized that STN deep brain stimulation (DBS) at individualized γ frequencies would improve whereas θ or β frequencies would worsen PD motor signs.

METHODS

We prospectively studied 13 patients with PD. STN local field potential (LFP) was recorded after electrode implantations, in the OFF and then in ON dopaminergic medication states while patients performed wrist movements. Six individual peak frequencies of the STN LFP power spectra were obtained: the greatest decrease in θ and β and greatest increase in γ frequencies in the ON state (MED) and during movements (MOVE). Eight DBS frequencies were applied including 6 MED and MOVE frequencies, high frequency (HF) used for chronic stimulation, and no stimulation. The patients were assessed using the motor Unified Parkinson's Disease Rating Scale (mUPDRS).

RESULTS

STN DBS at γ frequencies (MED and MOVE) and HF significantly improved mUPDRS scores compared to no stimulation and both γ frequencies were not different from HF. DBS at θ and β frequencies did not worsen mUPDRS scores compared to no stimulation.

CONCLUSION

Short-term administration of STN DBS at peak dopamine-dependent or movement-related γ frequencies were as effective as HF for reducing parkinsonian motor signs but DBS at θ and β frequencies did not worsen PD motor signs.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that STN DBS at patient-specific γ frequencies and at usual high frequencies both improved mUPDRS scores compared to no stimulation and did not differ in effect.

Agent-specific impact of single trades in financial markets

We present an analysis of the price impact associated with single trades effected by different financial firms. Using data from the Spanish Stock Market, we find a high degree of heterogeneity across different market members, both in the instantaneous impact functions and in the time-dependent market response to trades by individual members. This heterogeneity is statistically incompatible with the existence of market-wide universal impact dynamics that apply uniformly to all trades and suggest that, rather, market dynamics emerge from the complex interaction of different behaviors of market participants. Several possible reasons for this are discussed, along with potential extensions one may consider to increase the range of applicability of existing models of market impact.

Social features of online networks: the strength of intermediary ties in online social media

An increasing fraction of today's social interactions occur using online social media as communication channels. Recent worldwide events, such as social movements in Spain or revolts in the Middle East, highlight their capacity to boost people's coordination. Online networks display in general a rich internal structure where users can choose among different types and intensity of interactions. Despite this, there are still open questions regarding the social value of online interactions. For example, the existence of users with millions of online friends sheds doubts on the relevance of these relations. In this work, we focus on Twitter, one of the most popular online social networks, and find that the network formed by the basic type of connections is organized in groups. The activity of the users conforms to the landscape determined by such groups. Furthermore, Twitter's distinction between different types of interactions allows us to establish a parallelism between online and offline social networks: personal interactions are more likely to occur on internal links to the groups (the weakness of strong ties); events transmitting new information go preferentially through links connecting different groups (the strength of weak ties) or even more through links connecting to users belonging to several groups that act as brokers (the strength of intermediary ties).