Buzz: Face-to-Face Contact and the Urban Economy

This paper argues that existing models of urban concentrations are incomplete unless grounded in the most fundamental aspect of proximity; face-to-face contact. Face-to-face contact has four main features; it is an efficient communication technology; it can help solve incentive problems; it can facilitate socialization and learning; and it provides psychological motivation. We discuss each of these features in turn, and develop formal economic models of two of them. Face-to-face is particularly important in environments where information is imperfect, rapidly changing, and not easily codified, key features of many creative activities.Agglomeration, clustering, urban economics, face-to-face

A method for the dynamic management of genetic variability in dairy cattle

According to the general approach developed in this paper, dynamic management of genetic variability in selected populations of dairy cattle is carried out for three simultaneous purposes: procreation of young bulls to be further progeny-tested, use of service bulls already selected and approval of recently progeny-tested bulls for use. At each step, the objective is to minimize the average pairwise relationship coefficient in the future population born from programmed matings and the existing population. As a common constraint, the average estimated breeding value of the new population, for a selection goal including many important traits, is set to a desired value. For the procreation of young bulls, breeding costs are additionally constrained. Optimization is fully analytical and directly considers matings. Corresponding algorithms are presented in detail. The efficiency of these procedures was tested on the current Norman population. Comparisons between optimized and real matings, clearly showed that optimization would have saved substantial genetic variability without reducing short-term genetic gains

On the relevance of two manual tumor volume estimation methods for diffuse low-grade gliomas

International audienceManagement of Diffuse Low-Grade Glioma (DLGG) relies extensively on tumor volume estimation from MRI datasets. Two methods are currently clinically used to define this volume: the commonly used three-diameters solution and the more rarely used software-based volume reconstruction from the manual segmentations approach. We conducted an initial study of inter-practitioners' variability of software-based manual segmentations on DLGGs MRI datasets. A panel of 13 experts from various specialties and years of experience delineated 12 DLGGs' MRI scans. A statistical analysis on the segmented tumor volumes and pixels indicated that the individual practitioner, the years of experience and the specialty seem to have no significant impact on the segmentation of DLGGs. This is an interesting result as it had not yet been demonstrated and as it encourages cross-disciplinary collaboration. Our second study was with the three-diameters method, investigating its impact and that of the software-based volume reconstruction from manual segmentations method on tumor volume. We relied on the same dataset and on a participant from the first study. We compared the average of tumor volumes acquired by software reconstruction from manual segmentations method with tumor volumes obtained with the three-diameters method. We found that there is no statistically significant difference between the volumes estimated with the two approaches. These results correspond to non-operated and easily delineable DLGGs and are particularly interesting for time-consuming CUBE MRIs. Nonetheless, the three-diameters method has limitations in estimating tumor volumes for resected DLGGs, for which case the software-based manual segmentation method becomes more appropriate

Evaluation statistique de la segmentation manuelle de données IRM de gliomes diffus de bas grade

National audienceLes gliomes diffus de bas grade sont des tumeurs cérébrales primitives rares des adultes. La segmentation manuelle est essentielle pour le suivi des patients atteints de cette tumeur et pour le choix du traitement optimal. Cette méthode étant chronophage, il semble difficile de l'inclure dans la routine clinique. La segmentation automatique apparaît donc comme une solution potentielle pour répondre à cette problématique. Cependant, les algorithmes actuels de segmentation automatique n'ont pas encore prouvé leur efficacité pour les gliomes diffus de bas grade en raison de la spécificité de ce type de tumeurs. De ce fait, la segmentation manuelle demeure, aujourd'hui, la seule vérité terrain dans ce domaine. Une alternative pour contourner la perte en temps liée à la segmentation manuelle serait de partager la tâche entre différents praticiens, à condition que cette dernière soit reproductible. Le but de notre travail est d'évaluer la reproductibilité de la segmentation manuelle des examens IRM de gliomes diffus de bas grade, en fonction des praticiens, de leur expérience et de leur spécialité. Dans ce travail, nous avons conduit une étude statistique sur les volumes tumoraux d'un panel de 14 experts ayant manuellement segmenté 12 examens IRM de gliomes diffus de bas grade en utilisant le logiciel OsiriX. La plupart des études de segmentation de tumeurs cérébrales publiées mélangent différents types de tumeurs et comparent la segmentation automatique à la segmentation manuelle. Notre étude, au contraire, se focalise uniquement sur les gliomes diffus de bas grade et sur leur segmentation manuelle, car ce sont les plus difficiles à délimiter en raison de leur nature invasive. Une analyse statistique a fourni des résultats prometteurs en démontrant que les facteurs praticien, spécialité médi-cale et nombre d'années d'expérience n'ont pas d'impact significatif sur les valeurs moyennes de la variable volume tumoral

Modèles prédictifs pour les gliomes diffus de bas grade sous chimiothérapie

National audienceLes gliomes diffus de bas grade sont des tumeurs cérébrales primitives rares des adultes. Ces tumeurs progressent de manière continue au cours du temps et se trans-forment, par la suite, en tumeurs de grade supérieur dont la malignité est associée à un handicap neurologique et à une issue fatale. La taille de la tumeur est l'un des facteurs pronostiques les plus importants. De ce fait, il est d'une grande importance d'évaluer le volume tumoral pendant le suivi des patients. On recommande, pour ce faire, l'utilisation de l'IRM comme modalité. En outre, si la chirurgie reste la première option thérapeutique pour les gliomes diffus de bas grade, la chimiothérapie est de plus en plus utilisée (avant ou après une chirurgie potentielle). Ce-pendant, des questions cruciales et difficiles restent à ré-soudre : l'identification de sous-groupes de patients qui pourraient bénéficier de la chimiothérapie, la détermination du meilleur moment pour entamer une chimiothérapie, la définition de la durée de la chimiothérapie et l'évaluation du meilleur moment pour effectuer une chirurgie ou, le cas échéant, une radiothérapie. Dans ce travail, nous nous proposons d'aider les cliniciens dans la phase de prise de décision, en concevant de nouveaux modèles prédictifs dédiés à l'évolution du diamètre tumoral. Nous proposons deux modèles statistiques (linéaires et exponentiels) que nous avons testés sur une base de données de 16 patients dont la chimiothérapie a duré entre 14 et 32 mois, avec une durée moyenne de 22,8125 mois. Le choix du modèle le plus approprié a été réalisé avec le critère d'information d'Akaike corrigé. Les résultats sont très prometteurs, avec des coefficients de détermination, pour le modèle linéaire, variant entre 0,79 et 0,97 et une valeur moyenne de 0,90. Cela montre qu'il est possible d'alerter le clinicien sur un changement de la dynamique du diamètre tumoral

Towards a decision-aid tool in the case of chemotherapy treatment for low-grade glioma

International audienceDiffuse low-grade gliomas are rare brain tumors of young adults. Several treatments are used by the neuro oncologist (surgery, chemotherapy, radiotherapy). Our goal is to create a decision-aid tool to ensure an individualized treatment strategy.In clinical practice, the monitoring of gliomas is based on the estimation of tumor volume, obtained from MRI. This is done either through the three diameters method, or through a manual segmentation followed by a software reconstruction ; a subjective test helped us to compare statistically the two methods. We explore also semi-automatic segmentation algorithms which seem to be a promising way.Once we studied the reliability in the calculation of the interest variable, we are interested in the modeling of the evolution of the tumor’s size, in order to help oncologists in decision making. Crucial questions include identifying subgroups of patients who could benefit from chemotherapy, determining the best time to initiate or end chemotherapy, ... Our aim is to design new predictive models dedicated to the evolution of the tumor. Preliminary but very promising results have been obtained by regression models on a database of 55 patients under neoadjuvant chemotherapy treatment. Two statistical models (linear and exponential) have been identified

Statistical evaluation of manual segmentation of a diffuse low-grade glioma MRI dataset

International audienceSoftware-based manual segmentation is critical to the supervision of diffuse low-grade glioma patients and to the optimal treatment’s choice. However, manual segmentationbeing time-consuming, it is difficult to include it in the clinicalroutine. An alternative to circumvent the time cost of manualsegmentation could be to share the task among different practitioners, providing it can be reproduced. The goal of our work is to assess diffuse low-grade gliomas’ manual segmentation’s reproducibility on MRI scans, with regard to practitioners, their experience and field of expertise. A panel of 13 experts manually segmented 12 diffuse low-grade glioma clinical MRI datasets using the OSIRIX software. A statistical analysis gave promising results, as the practitioner factor, the medical specialty and the years of experience seem to have no significant impact on the average values of the tumor volume variable

Predictive models for diffuse low-grade glioma patients under chemotherapy

International audienceDiffuse low-grade gliomas are rare primitive cerebral tumours of adults. These tumors progress continuously over time and then turn to a higher grade of malignancy associated with neurological disability, leading ultimately to death. Tumour size is one of the most important prognostic factors. Thus, it is of great importance to be able to assess the volume of the tumor during the patients’ monitoring.MRI is nowadays the recommended modality to achieve this. Furthermore, if surgery remains the first option for diffuse low-grade gliomas, chemotherapy is increasingly used (before or after a possible surgery). However, crucial and difficult questions remain to be answered: identifying subgroups ofpatients who could benefit from chemotherapy, determining the best time to initiate chemotherapy, defining the duration of chemotherapy and evaluating the optimal time to perform surgery, or otherwise radiotherapy. In this study, we propose to help clinicians in decision-making, by designing new predictivemodels dedicated to the evolution of the diameter of the tumor. Two proposed statistical models (linear and exponential) have been validated on a database of 16 patients whose temozolomide-based chemotherapy lasted between 14 and 32 months, with an average duration of 22.8 months. The selection of the most appropriate model has been achieved with the corrected Akaike’s Information Criterion. The results are very promising, with coefficients of determination varying from 0.79 to 0.97 with an average value of 0.90 for the linear model. This shows it is possible to alert the clinician to a change in the tumor diameter’s dynamics

MUSiC : a model-unspecific search for new physics in proton-proton collisions at root s=13TeV

Results of the Model Unspecific Search in CMS (MUSiC), using proton-proton collision data recorded at the LHC at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1), are presented. The MUSiC analysis searches for anomalies that could be signatures of physics beyond the standard model. The analysis is based on the comparison of observed data with the standard model prediction, as determined from simulation, in several hundred final states and multiple kinematic distributions. Events containing at least one electron or muon are classified based on their final state topology, and an automated search algorithm surveys the observed data for deviations from the prediction. The sensitivity of the search is validated using multiple methods. No significant deviations from the predictions have been observed. For a wide range of final state topologies, agreement is found between the data and the standard model simulation. This analysis complements dedicated search analyses by significantly expanding the range of final states covered using a model independent approach with the largest data set to date to probe phase space regions beyond the reach of previous general searches.Peer reviewe

Measurement of prompt open-charm production cross sections in proton-proton collisions at root s=13 TeV

The production cross sections for prompt open-charm mesons in proton-proton collisions at a center-of-mass energy of 13TeV are reported. The measurement is performed using a data sample collected by the CMS experiment corresponding to an integrated luminosity of 29 nb(-1). The differential production cross sections of the D*(+/-), D-+/-, and D-0 ((D) over bar (0)) mesons are presented in ranges of transverse momentum and pseudorapidity 4 < p(T) < 100 GeV and vertical bar eta vertical bar < 2.1, respectively. The results are compared to several theoretical calculations and to previous measurements.Peer reviewe