Search

Abstract What predicts a neighborhood’s adaptability to essential public health policies and shelter-in-place regulations that prevent the harmful spread of COVID-19? To answer this question, we present a novel application of human mobility patterns and human behavior in a network setting. We analyze 2 years of mobility data (January 2019 to December 2020) from New York City and construct weekly mobility networks between census block groups based on aggregated point-of-interest visit patterns. Our results indicate that neighborhoods’ socioeconomic and geographic characteristics play a significant role in predicting their adaptability to active shelter-in-place policies. Our simulation outcomes reveal that, alongside factors such as race, education, and income, the geographical attributes of neighborhoods, such as access to amenities that satisfy community needs are equally important factors in predicting neighborhood adaptability to public health policies. These findings offer valuable insights that can enhance urban planning strategies, thereby aiding pandemic mitigation efforts and fostering increased adaptability of urban areas in the face of exogenous shocks like the COVID-19 pandemic.

Abstract Diversity has tremendous value in modern society. Economic theories suggest that cultural and ethnic diversity may contribute to economic development and prosperity. To date, however, the correspondence between diversity measures and the economic indicators, such as the Consumer Price Index, has not been quantified. This is primarily due to the difficulty in obtaining data on the micro behaviors and macroeconomic indicators. In this paper, we explore the relationship between diversity measures extracted from large-scale and high-resolution mobile phone data, and the CPIs in different sectors in a tourism country. Interestingly, we show that diversity measures associate strongly with the general and sectoral CPIs, using phone records in Andorra. Based on these strong predictive relationships, we construct daily, and spatial maps to monitor CPI measures at a high resolution to complement existing CPI measures from the statistical office. The case study on Andorra used in this study contributes to two growing literature: linking diversity with economic outcomes, and macro-economic monitoring with large-scale data. Future study is required to examine the relationship between the two measures in other countries.

The recent rise of using digital representations for products and processes has created a movement to use 'digital twins' for organization design. We provide an overview of the notion of digital twin as a synchronized, real-time two-way interacting digital representation of the real-world phenomenon it is expected to replicate as a twin. The claim of a two-way causal connection between the real-world and the digital representation makes the current rhetoric about Digital Twins especially problematic. To grasp the challenges involved in Digital Twins of Organizations (DTO), we start from Digital Twins of Things (DTT) and Digital Twins of Business Processes (DTBP). We analyze and compare different kinds of digital twins using Peircean theory of semiotic relationships, which differentiate between signals, icons, and symbols. We posit that in order to fully model organizations as digital twins, an organization designer needs to model features of organizations that are not present in DTTs and DTBPs, such as agency, conflict, and emergence. Given the inevitable presence of symbolic phenomena, we speculate to what extent it is possible to move towards full DTOs, what characteristics broader DTOs need to have, and what benefits more extensive use of DTOs will offer for organization designers. We finally offer pointers towards a research agenda for DTOs that have the potential to improve organization designs and contribute to theory on organization design. [ABSTRACT FROM AUTHOR]

Itch is a common symptom of dermatologic diseases associated with significant impairment of health-related quality of life (QoL). This study reveals disparities in itch symptom experience and itch impact on QoL. A retrospective study of patient-reported outcome measure (PRO) data (ItchyQoL, Itch NRS, Pain Interference, Anxiety) for 387 outpatient dermatology visits to characterize the impact of itch on patients' QoL and itch symptom experience based on skin color in patients with dermatologic disease. Most patients were Caucasian females (67%) with mean age of 48 years. Correlative analyses showed mild itch associated with emotional impacts on QoL (p < 0.01), while severe itch associated with functional and emotional impacts on QoL (p < 0.01). African American (AA) patients reported more "severe-range" answers for 15 (68%) ItchyQoL items and had higher ItchyQoL mean scores (p = 0.001). ItchyQoL demonstrated an emotional impact on QoL by mild itch, but a functional and emotional impact on QoL by severe itch. Further, AAs suffered from greater itch-related impairment in QoL than Caucasian patients, especially due to scarring and sleeplessness. [ABSTRACT FROM AUTHOR]

Powerful actors have engaged in information control for centuries, restricting, promoting, or influencing the information environment as it suits their evolving agendas. In the Digital Age, information control has moved online, and information operations now target the online platforms that play a critical role in news engagement and civic debate. In this paper, we use a discrete-time stochastic model to analyze coordinated activity in an online social network, representing the behaviors of accounts as interacting Markov chains. From a dataset of 31,521 tweets posted by 206 accounts, half of which were identified by Twitter as participating in a state-linked information operation, we evaluate the coordination, measured by the apparent influence, between pairs of state-linked accounts compared to unaffiliated accounts. Our analysis reveals that state-linked actors demonstrate significantly higher levels of coordination among themselves compared to their coordination with unaffiliated accounts. Furthermore, the degree of coordination observed between state-linked accounts is more than seven times greater than the coordination observed between unaffiliated accounts. Moreover, we find that the account that represented the most coordinated activity in the network had no followers, demonstrating the power of our modeling approach to unearth hidden connections even in the absence of explicit network structure. [ABSTRACT FROM AUTHOR]

This is a first-of-its-kind dataset containing detailed purchase histories from 5027 U.S. Amazon.com consumers, spanning 2018 through 2022, with more than 1.8 million purchases. Consumer spending data are customarily collected through government surveys to produce public datasets and statistics, which serve public agencies and researchers. Companies now collect similar data through consumers' use of digital platforms at rates superseding data collection by public agencies. We published this dataset in an effort towards democratizing access to rich data sources routinely used by companies. The data were crowdsourced through an online survey and shared with participants' informed consent. Data columns include order date, product code, title, price, quantity, and shipping address state. Each purchase history is linked to survey data with information about participants' demographics, lifestyle, and health. We validate the dataset by showing expenditure correlates with public Amazon sales data (Pearson r = 0.978, p < 0.001) and conduct analyses of specific product categories, demonstrating expected seasonal trends and strong relationships to other public datasets. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. Using large-scale mobility data, the authors examine how the quality of food in mobile environments away from home affects food choice. [ABSTRACT FROM AUTHOR]

What predicts a neighborhood's adaptability to essential public health policies and shelter-in-place regulations that prevent the harmful spread of COVID-19? To answer this question, we present a novel application of human mobility patterns and human behavior in a network setting. We analyze 2 years of mobility data (January 2019 to December 2020) from New York City and construct weekly mobility networks between census block groups based on aggregated point-of-interest visit patterns. Our results indicate that neighborhoods' socioeconomic and geographic characteristics play a significant role in predicting their adaptability to active shelter-in-place policies. Our simulation outcomes reveal that, alongside factors such as race, education, and income, the geographical attributes of neighborhoods, such as access to amenities that satisfy community needs are equally important factors in predicting neighborhood adaptability to public health policies. These findings offer valuable insights that can enhance urban planning strategies, thereby aiding pandemic mitigation efforts and fostering increased adaptability of urban areas in the face of exogenous shocks like the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Scholars and practitioners have described how investing in health care earlier rather than later can be beneficial, from how "biomarkers" offer promise for early disease detection to healthcare system "incentives" that can promote early preventive medicine. Work by health economists has also made clear that the "health capital" of an individual depreciates over time in the absence of investments in health. Yet, our current policy makers and healthcare system continue prioritizing care of late-stage complex symptomatic illness, often when cure is impossible and disease reversal is improbable, thus exacerbating public health burdens. Critically missing are predicates to address this challenge include the following: first, identifying and validating the specific set of presymptomatic biomarkers that will inform the most appropriate intervention timing for those medical conditions amenable to early intervention; second, shifting fundamental health economic incentives to influence the appropriate disease prevention market; and third, formulating and executing a viable economic framework of reimbursement. We examine these predicates and propose actionable policy recommendations that may help align stakeholder interests to improve public health. [ABSTRACT FROM AUTHOR]

Our study utilizes network science to examine how uneven vaccine distribution affects mass vaccination strategies in the United States. Using mobility network data and epidemiological models, we find that distributing a fixed quantity of additional vaccines across Census Block Groups (CBGs) can vary case count reductions by up to 200%. This highlights the impact of vaccination heterogeneity in mobility networks on epidemic outcomes. Our efficient algorithm identifies optimal vaccine distribution for maximum case reduction. Simulations show a possible 9.5% decrease in case numbers with just a 1% increase in the national vaccination rate if vaccines are optimally distributed. This result surpasses those from other vaccine distribution models. Our findings underline the need for policymakers to understand the interaction between vaccination patterns and mobility networks, suggesting that grasping geographical vaccine uptake variations could be as crucial as raising the overall vaccination rate. The inhomogeneity of vaccine uptake has many roots from social, political to economic and analysing the consequent impact on virus proliferation is complex. Here, using epidemiological models based on census block group level mobility data the authors demonstrate that a targeted increase of vaccinations for critical locations results in a greater reduction in COVID-19 cases. [ABSTRACT FROM AUTHOR]

Small and Medium-sized Enterprises play a significant role in most economies by contributing to job creation and economic growth. A majority of such merchants rely on business financing, and thus, financial institutions and investors need to assess their performance before making decisions on business loans. However, current methods of predicting merchants' future performance involve their private internal information, such as revenue and customer base, which cannot be shared without potentially exposing critical information. To address this problem, we first propose a novel approach to predicting merchants' future performance using credit card transaction data. Specifically, we construct a merchant network, regarding customers as bridges between merchants, and extract features from the constructed network structure for prediction purposes. Our study results demonstrate that the performance of machine learning models with features extracted from our proposed network is comparable to those with conventional revenue- and customer-based features, while maintaining higher privacy levels when shared with third-party organizations. Our approach offers a practical solution to privacy concerns over data and information required for merchants' performance prediction, enabling safe data-sharing among financial institutions and investors, helping them make more informed decisions on allocating their financial resources while ensuring that merchants' sensitive information is kept confidential. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. Mobile phone data reveals a significant decrease in the income diversity of urban encounters during the COVID-19 pandemic in the USA, even though overall mobility returned to pre-pandemic levels by late 2021. This was mainly due to persistent behavioral changes including less willingness to explore new places. [ABSTRACT FROM AUTHOR]

The structure of networks has been a focal research topic over the past few decades. These research efforts have enabled the discovery of numerous structural patterns and regularities, bringing forth advancements in many fields. In particular, the ubiquitous power-law patterns evident in degree distributions, graph eigenvalues and human mobility patterns have provided the opportunity to model many different complex systems. However, regularities in the dynamical patterns of networks remain a considerably less explored terrain. In this study we examine the dynamics of networks, focusing on stability characteristics of node popularity, and present our results using various empirical datasets. Specifically, we address several intriguing questions – for how long are popular nodes expected to remain so? How much time is expected to pass between two consecutive popularity periods? What characterizes nodes which manage to maintain their popularity for long periods of time? Surprisingly, we find that such temporal aspects are governed by a power-law regime, and that these power-law regularities are equally likely across all node ages. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Long ties, the social ties that bridge different communities, are widely believed to play crucial roles in spreading novel information in social networks. However, some existing network theories and prediction models indicate that long ties might dissolve quickly or eventually become redundant, thus putting into question the long-term value of long ties. Our empirical analysis of real-world dynamic networks shows that contrary to such reasoning, long ties are more likely to persist than other social ties, and that many of them constantly function as social bridges without being embedded in local networks. Using a cost-benefit analysis model combined with machine learning, we show that long ties are highly beneficial, which instinctively motivates people to expend extra effort to maintain them. This partly explains why long ties are more persistent than what has been suggested by many existing theories and models. Overall, our study suggests the need for social interventions that can promote the formation of long ties, such as mixing people with diverse backgrounds. Social network analysis has been long involved in studying social ties. The authors study the dynamics of long ties using mobile phone dataset, finding that long ties typically persist for longer than short ties, suggesting implications for social intervention to support long ties. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. Mobility restrictions implemented to reduce the spread of COVID-19 have significantly impacted walking behavior. In this study, the authors integrated mobility data from mobile devices and area-level data to study the walking patterns of 1.62 million anonymous users in 10 US metropolitan areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Patient-reported outcome (PRO) measures play an important role in clinical care. Currently, a broad-spectrum, validated PRO measure suitable for all dermatology patients, as part of clinical care, does not exist. Patient-reported Outcome Measures Information System (PROMIS) measures track specific domain outcomes across all diseases. To assess the relevance and utility of a computer-adaptive health assessment consisting of three PROMIS domains in routine dermatologic care. This retrospective study evaluated a PROMIS health assessment, consisting of three computer-adaptive test domains (pain interference, anxiety, and depression), administered as part of routine clinical care in three dermatology clinics at an academic medical center. The primary objective was to identify clinically significant associations between high PROMIS domain scores (i.e., t score > 55) and dermatologic disease, as well as change in PROMIS domain scores in response to treatment. The majority of patients who initiated the assessment completed all domains (88.7%). In patients with atopic dermatitis, acne, hidradenitis suppurativa, and psoriasis, high PROMIS scores correlated with clinically relevant outcomes, such as severe disease, unsuccessful treatment, uncontrolled disease, and the presence of a mental health condition. PROMIS Pain Interference, anxiety and depression identified patients with severe disease, unsuccessful treatment regimens, poorly-controlled disease, and/or mental health comorbidities for multiple skin conditions. Further utilization of PROMIS domains in routine clinical care will promote patient-centered care and improve quality of care. [ABSTRACT FROM AUTHOR]

Social networks are known to be assortative with respect to many attributes, such as age, weight, wealth, level of education, ethnicity and gender: Similar people according to these attributes tend to be more connected. This can be explained by influences and homophily. Independently of its origin, this assortativity gives us information about each node given its neighbors. Assortativity can thus be used to improve individual predictions in a broad range of situations, when data are missing or inaccurate. This paper presents a general framework based on probabilistic graphical models to exploit social network structures for improving individual predictions of node attributes. Using this framework, we quantify the assortativity range leading to an accuracy gain in several situations, with various individual prediction profiles. We finally show how specific characteristics of the network can enhance performances further. For instance, the gender assortativity in real-world mobile phone data drastically changes according to some communication attributes. In this case, using the network topology indeed improves local predictions of node labels and moreover enables inferring missing node labels based on a subset of known vertices. In both cases, the performances of the proposed method are statistically significantly superior to the ones achieved by state-of-the-art label propagation and feature extraction schemes in most settings. [ABSTRACT FROM AUTHOR]

Urban income segregation is a widespread phenomenon that challenges societies across the globe. Classical studies on segregation have largely focused on the geographic distribution of residential neighborhoods rather than on patterns of social behaviors and interactions. In this study, we analyze segregation in economic and social interactions by observing credit card transactions and Twitter mentions among thousands of individuals in three culturally different metropolitan areas. We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both purchase activity and online communication. Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric for purchase activity, i.e., the amount of interaction from poorer to wealthier neighborhoods is larger than vice versa. Our results provide novel insights into the understanding of behavioral segregation in human interactions with significant socio-political and economic implications. [ABSTRACT FROM AUTHOR]

Purpose: Although topical agents are often provided during radiation therapy, there is limited consensus and evidence for their use prophylactically to prevent or reduce radiation dermatitis.Methods: This was a multi-site, randomized, placebo-controlled, blinded study of 191 breast cancer patients to compare the prophylactic effectiveness of three topical agents (Curcumin, HPR Plus™, and Placebo) for reducing radiation dermatitis and associated pain. Patients applied the topical agent to their skin in the radiation area site three times daily starting the first day of radiation therapy (RT) until 1 week after RT completion.Results: Of the 191 randomized patients, 171 patients were included in the final analyses (87.5% white females, mean age = 58 (range = 36-88)). Mean radiation dermatitis severity (RDS) scores did not significantly differ between study arms (Curcumin = 2.68 [2.49, 2.86]; HPR Plus™ = 2.64 [2.45, 2.82]; Placebo = 2.63 [2.44, 2.83]; p = 0.929). Logistic regression analyses showed that increased breast field separation positively correlated with increased radiation dermatitis severity (p = 0.018). In patients with high breast field separation (≥ 25 cm), RDS scores (Curcumin = 2.70 [2.21, 3.19]; HPR Plus™ = 3.57 [3.16, 4.00]; Placebo = 2.95 [2.60, 3.30]; p = 0.024) and pain scores (Curcumin = 0.52 [- 0.28, 1.33]; HPR Plus™ = 0.55 [- 0.19, 1.30]; Placebo = 1.73 [0.97, 2.50]; p = 0.046) significantly differed at the end of RT.Conclusions: Although there were no significant effects of the treatment groups on the overall population, our exploratory subgroup analysis suggests that prophylactic treatment with topical curcumin may be effective for minimizing skin reactions and pain for patients with high breast separation (≥ 25 cm) who may have the worst skin reactions. [ABSTRACT FROM AUTHOR]

Much recent work has illuminated the growth, innovation, and prosperity of entire cities, but there is relatively less evidence concerning the growth and prosperity of individual neighborhoods. In this paper we show that diversity of amenities within a city neighborhood, computed from openly available points of interest on digital maps, accurately predicts human mobility ("flows") between city neighborhoods and that these flows accurately predict neighborhood economic productivity. Additionally, the diversity of consumption behaviour or the diversity of flows together with geographic centrality and population density accurately predicts neighborhood economic growth, even after controlling for standard factors such as population, etc. We develop our models using geo-located purchase data from Istanbul, and then validate the relationships using openly available data from Beijing and several U.S. cities. Our results suggest that the diversity of goods and services within a city neighborhood is the largest single factor driving both human mobility and economic growth. [ABSTRACT FROM AUTHOR]

Centrality is a fundamental network property that ranks nodes by their structural importance. However, the network structure alone may not predict successful diffusion in many applications, such as viral marketing and political campaigns. We propose contextual centrality, which integrates structural positions, the diffusion process, and, most importantly, relevant node characteristics. It nicely generalizes and relates to standard centrality measures. We test the effectiveness of contextual centrality in predicting the eventual outcomes in the adoption of microfinance and weather insurance. Our empirical analysis shows that the contextual centrality of first-informed individuals has higher predictive power than that of other standard centrality measures. Further simulations show that when the diffusion occurs locally, contextual centrality can identify nodes whose local neighborhoods contribute positively. When the diffusion occurs globally, contextual centrality signals whether diffusion may generate negative consequences. Contextual centrality captures more complicated dynamics on networks than traditional centrality measures and has significant implications for network-based interventions. [ABSTRACT FROM AUTHOR]

The Northern Hemisphere dominates our knowledge of Mesozoic and Cenozoic fossilized tree resin (amber) with few findings from the high southern paleolatitudes of Southern Pangea and Southern Gondwana. Here we report new Pangean and Gondwana amber occurrences dating from ~230 to 40 Ma from Australia (Late Triassic and Paleogene of Tasmania; Late Cretaceous Gippsland Basin in Victoria; Paleocene and late middle Eocene of Victoria) and New Zealand (Late Cretaceous Chatham Islands). The Paleogene, richly fossiliferous deposits contain significant and diverse inclusions of arthropods, plants and fungi. These austral discoveries open six new windows to different but crucial intervals of the Mesozoic and early Cenozoic, providing the earliest occurrence(s) of some taxa in the modern fauna and flora giving new insights into the ecology and evolution of polar and subpolar terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

Global financial crises have led to the understanding that classical econometric models are limited in comprehending financial markets in extreme conditions, partially since they disregarded complex interactions within the system. Consequently, in recent years research efforts have been directed towards modeling the structure and dynamics of the underlying networks of financial ecosystems. However, difficulties in acquiring fine-grained empirical financial data, due to regulatory limitations, intellectual property and privacy control, still hinder the application of network analysis to financial markets. In this paper we study the trading of cryptocurrency tokens on top of the Ethereum Blockchain, which is the largest publicly available financial data source that has a granularity of individual trades and users, and which provides a rare opportunity to analyze and model financial behavior in an evolving market from its inception. This quickly developing economy is comprised of tens of thousands of different financial assets with an aggregated valuation of more than 500 Billion USD and typical daily volume of 30 Billion USD, and manifests highly volatile dynamics when viewed using classic market measures. However, by applying network theory methods we demonstrate clear structural properties and converging dynamics, indicating that this ecosystem functions as a single coherent financial market. These results suggest that a better understanding of traditional markets could become possible through the analysis of fine-grained, abundant and publicly available data of cryptomarkets. [ABSTRACT FROM AUTHOR]

Bees play a key role in pollination of crops and in diverse ecosystems. There have been multiple reports in recent years illustrating bee population declines worldwide. The search for more accurate forecast models can aid both in the understanding of the regular behavior and the adverse situations that may occur with the bees. It also may lead to better management and utilization of bees as pollinators. We address an investigation with Recurrent Neural Networks in the task of forecasting bees' level of activity taking into account previous values of level of activity and environmental data such as temperature, solar irradiance and barometric pressure. We also show how different input time windows, algorithms of attribute selection and correlation analysis can help improve the accuracy of our model. [ABSTRACT FROM AUTHOR]

Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. Understanding the behaviour of artificial intelligence systems is essential to our ability to control their actions, reap their benefits and minimize their harms. Here we argue that this necessitates a broad scientific research agenda to study machine behaviour that incorporates and expands upon the discipline of computer science and includes insights from across the sciences. We first outline a set of questions that are fundamental to this emerging field and then explore the technical, legal and institutional constraints on the study of machine behaviour. Understanding the behaviour of the machines powered by artificial intelligence that increasingly mediate our social, cultural, economic and political interactions is essential to our ability to control the actions of these intelligent machines, reap their benefits and minimize their harms. [ABSTRACT FROM AUTHOR]

The combination of increased availability of large amounts of fine-grained human behavioral data and advances in machine learning is presiding over a growing reliance on algorithms to address complex societal problems. Algorithmic decision-making processes might lead to more objective and thus potentially fairer decisions than those made by humans who may be influenced by greed, prejudice, fatigue, or hunger. However, algorithmic decision-making has been criticized for its potential to enhance discrimination, information and power asymmetry, and opacity. In this paper, we provide an overview of available technical solutions to enhance fairness, accountability, and transparency in algorithmic decision-making. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy-makers, and citizens to co-develop, deploy, and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness and transparency. In doing so, we describe the Open Algortihms (OPAL) project as a step towards realizing the vision of a world where data and algorithms are used as lenses and levers in support of democracy and development. [ABSTRACT FROM AUTHOR]

Purpose: Despite advances in medical technology, radiation dermatitis occurs in 95% of patients receiving radiation therapy (RT) for cancer. Currently, there is no standard and effective treatment for the prevention or control of radiation dermatitis. The goal of the study was to determine the efficacy of oral curcumin, one of the biologically active components in turmeric, at reducing radiation dermatitis severity (RDS) at the end of RT, using the RDS scale, compared to placebo.Methods: This was a multisite, randomized, double-blinded, placebo-controlled trial of 686 breast cancer patients. Patients took four 500-mg capsules of placebo or curcumin three times daily throughout their prescribed course of RT until 1 week post-RT.Results: A total of 686 patients were included in the final analyses (87.5% white females, mean age = 58). Linear mixed-model analyses demonstrated that curcumin did not reduce radiation dermatitis severity at the end of RT compared to placebo (B (95% CI) = 0.044 (− 0.101, 0.188), p = 0.552). Fewer curcumin patients with RDS > 3.0 suggested a trend toward reduced severity (7.4 vs. 12.9%, p = 0.082). Patient-reported changes in pain, symptoms, and quality of life were not statistically significant between arms.Conclusions: Oral curcumin did not significantly reduce radiation dermatitis severity compared to placebo. The skin rating variation and broad eligibility criteria could not account for the undetectable therapeutic effect. An objective measure for radiation dermatitis severity and further exploration for an effective treatment for radiation dermatitis is warranted. [ABSTRACT FROM AUTHOR]

Mobile phones are one of the fastest growing technologies in the developing world with global penetration rates reaching 90%. Mobile phone data, also called CDR, are generated everytime phones are used and recorded by carriers at scale. CDR have generated groundbreaking insights in public health, official statistics, and logistics. However, the fact that most phones in developing countries are prepaid means that the data lacks key information about the user, including gender and other demographic variables. This precludes numerous uses of this data in social science and development economic research. It furthermore severely prevents the development of humanitarian applications such as the use of mobile phone data to target aid towards the most vulnerable groups during crisis. We developed a framework to extract more than 1400 features from standard mobile phone data and used them to predict useful individual characteristics and group estimates. We here present a systematic cross-country study of the applicability of machine learning for dataset augmentation at low cost. We validate our framework by showing how it can be used to reliably predict gender and other information for more than half a million people in two countries. We show how standard machine learning algorithms trained on only 10,000 users are sufficient to predict individual's gender with an accuracy ranging from 74.3 to 88.4% in a developed country and from 74.5 to 79.7% in a developing country using only metadata. This is significantly higher than previous approaches and, once calibrated, gives highly accurate estimates of gender balance in groups. Performance suffers only marginally if we reduce the training size to 5,000, but significantly decreases in a smaller training set. We finally show that our indicators capture a large range of behavioral traits using factor analysis and that the framework can be used to predict other indicators of vulnerability such as age or socio-economic status. Mobile phone data has a great potential for good and our framework allows this data to be augmented with vulnerability and other information at a fraction of the cost. [ABSTRACT FROM AUTHOR]