High-statistics modeling of complex pedestrian avoidance scenarios

Quantitatively modeling the trajectories and behavior of pedestrians walking in crowds is an outstanding fundamental challenge deeply connected with the physics of flowing active matter, from a scientific point of view, and having societal applications entailing individual safety and comfort, from an application perspective. In this contribution, we review a pedestrian dynamics modeling approach, previously proposed by the authors, aimed at reproducing some of the statistical features of pedestrian motion. Comparing with high-statistics pedestrian dynamics measurements collected in real-life conditions (from hundreds of thousands to millions of trajectories), we modeled quantitatively the statistical features of the undisturbed motion (i.e. in absence of interactions with other pedestrians) as well as the avoidance dynamics triggered by a pedestrian incoming in the opposite direction. This was accomplished through (coupled) Langevin equations with potentials including multiple preferred velocity states and preferred paths. In this chapter we review this model, discussing some of its limitations, in view of its extension toward a more complex case: the avoidance dynamics of a single pedestrian walking through a crowd that is moving in the opposite direction. We analyze some of the challenges connected to this case and present extensions to the model capable of reproducing some features of the motion

Male tobacco smoke load and non-lung cancer mortality associations in Massachusetts

<p>Abstract</p> <p>Background</p> <p>Different methods exist to estimate smoking attributable cancer mortality rates (Peto and Ezzati methods, as examples). However, the smoking attributable estimates using these methods cannot be generalized to all population sub-groups. A simpler method has recently been developed that can be adapted and applied to different population sub-groups. This study assessed cumulative tobacco smoke damage (smoke load)/non-lung cancer mortality associations across time from 1979 to 2003 among all Massachusetts males and ages 30–74 years, using this novel methodology.</p> <p>Methods</p> <p>Annual lung cancer death rates were used as smoke load bio-indices, and age-adjusted lung/all other (non-lung) cancer death rates were analyzed with linear regression approach. Non-lung cancer death rates include all cancer deaths excluding lung. Smoking-attributable-fractions (SAFs) for the latest period (year 2003) were estimated as: 1-(estimated unexposed cancer death rate/observed rate).</p> <p>Results</p> <p>Male lung and non-lung cancer death rates have declined steadily since 1992. Lung and non-lung cancer death rates were tightly and steeply associated across years. The slopes of the associations analyzed were 1.69 (95% confidence interval (CI) 1.35–2.04, r = 0.90), and 1.36 (CI 1.14–1.58, r = 0.94) without detected autocorrelation (Durbin-Watson statistic = 1.8). The lung/non-lung cancer death rate associations suggest that all-sites cancer death rate SAFs in year 2003 were 73% (Sensitivity Range [SR] 61–82%) for all ages and 74% (SR 61–82%) for ages 30–74 years.</p> <p>Conclusion</p> <p>The strong lung/non-lung cancer death rate associations suggest that tobacco smoke load may be responsible for most prematurely fatal cancers at both lung and non-lung sites. The present method estimates are greater than the earlier estimates. Therefore, tobacco control may reduce cancer death rates more than previously noted.</p

High prevalence of lung cancer in a surgical cohort of lung cancer patients a decade after smoking cessation

<p>Abstract</p> <p>Background</p> <p>This study was designed to assess the prevalence of smoking at time of lung cancer diagnosis in a surgical patient cohort referred for cardiothoracic surgery.</p> <p>Methods</p> <p>Retrospective study of lung cancer patients (n = 626) referred to three cardiothoracic surgeons at a tertiary care medical center in Southern California from January 2006 to December 2008. Relationships among years of smoking cessation, smoking status, and tumor histology were analyzed with Chi-square tests.</p> <p>Results</p> <p>Seventy-seven percent (482) had a smoking history while 11.3% (71) were current smokers. The length of smoking cessation to cancer diagnosis was <1 year for 56 (13.6%), 1-10 years for 110 (26.8%), 11-20 years for 87 (21.2%), 21-30 years for 66 (16.1%), 31-40 years for 44 (10.7%), 41-50 years for 40 (9.7%) and 51-60 years for 8 (1.9%). The mean cessation was 18.1 ± 15.7 years (n = 411 former smokers). Fifty-nine percent had stage 1 disease and 68.0% had adenocarcinoma. Squamous cell carcinoma was more prevalent in smokers (15.6% vs. 8.3%, p = 0.028); adenocarcinoma was more prevalent in never-smokers (79.9% versus 64.3%, p = 0.0004). The prevalence of adenocarcinoma varied inversely with pack year (p < 0.0001) and directly with years of smoking cessation (p = 0.0005).</p> <p>Conclusions</p> <p>In a surgical lung cancer cohort, the majority of patients were smoking abstinent greater than one decade before the diagnosis of lung cancer.</p

A simple algebraic cancer equation: calculating how cancers may arise with normal mutation rates

<p>Abstract</p> <p>Background</p> <p>The purpose of this article is to present a relatively easy to understand cancer model where transformation occurs when the first cell, among many at risk within a colon, accumulates a set of driver mutations. The analysis of this model yields a simple algebraic equation, which takes as inputs the number of stem cells, mutation and division rates, and the number of driver mutations, and makes predictions about cancer epidemiology.</p> <p>Methods</p> <p>The equation [<it>p </it>= 1 - (1 - (1 - (1 - <it>u</it>)^<it>d</it>)^<it>k</it>)^{<it>Nm </it>}] calculates the probability of cancer (<it>p</it>) and contains five parameters: the number of divisions (<it>d</it>), the number of stem cells (<it>N </it>× <it>m</it>), the number of critical rate-limiting pathway driver mutations (<it>k</it>), and the mutation rate (<it>u</it>). In this model progression to cancer "starts" at conception and mutations accumulate with cell division. Transformation occurs when a critical number of rate-limiting pathway mutations first accumulates within a single stem cell.</p> <p>Results</p> <p>When applied to several colorectal cancer data sets, parameter values consistent with crypt stem cell biology and normal mutation rates were able to match the increase in cancer with aging, and the mutation frequencies found in cancer genomes. The equation can help explain how cancer risks may vary with age, height, germline mutations, and aspirin use. APC mutations may shorten pathways to cancer by effectively increasing the numbers of stem cells at risk.</p> <p>Conclusions</p> <p>The equation illustrates that age-related increases in cancer frequencies may result from relatively normal division and mutation rates. Although this equation does not encompass all of the known complexity of cancer, it may be useful, especially in a teaching setting, to help illustrate relationships between small and large cancer features.</p