Exhumation of the Sierra de Cameros (Iberian Range, Spain): constraints from low-temperature thermochronology

We present new fission-track and (U–Th)/He data from apatite and zircon in order to reconstruct the exhumation of the Sierra de Cameros, in the northwestern part of Iberian Range, Spain. Zircon fission-track ages from samples from the depocentre of the basin were reset during the metamorphic peak at approximately 100 Ma. Detrital apatites from the uppermost sediments retain fission-track age information that is older than the sediment deposition age, indicating that these rocks have not exceeded 110 8C. Apatites from deeper in the stratigraphic sequence of the central part of the basin have fission-track ages of around 40 Ma, significantly younger than the stratigraphic age, recording the time of cooling after peak metamorphic conditions. Apatite (U–Th)/He ages in samples from these sediments are 31–40 Ma and record the last period of cooling during Alpine compression. The modelled thermal history derived from the uppermost sediments indicates that the thermal pulse associated with peak metamorphism was rapid, and that the region has cooled continuously to the present. The estimated palaeogeothermal gradient is around 86 8C km21 and supports a tectonic model with a thick sedimentary fill (c. 8 km) and explains the origin of the low-grade metamorphism observed in the oldest sediments

New Perspectives for Pancreatic Cancer Treatment. Will We Be Able to Ensure Equity to Care?

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Editorial: New Frontiers for Artificial Intelligence in Surgical Decision Making and its Organizational Impacts

The purpose of the research topic call “New Frontiers for Artificial Intelligence in Surgical Decision Making and its Organizational Impacts “ was to collect the recent developments and undergoing studies in AI in surgery and surgical oncology. More in detail, the aim was to gather contributions on the advancement, deployment, use, and implementation of AI-based applications in surgical practice, understanding their potential contribution to clinical decision making. Moreover, the idea was to assess the potential impacts of such a technology on surgeons, other clinicians, patients, medical institutions, developers, and policy-makers, with an eye open to the organizational and educational consequences and opportunities

Reconciling long-term cultural diversity and short-term collective social behavior

An outstanding open problem is whether collective social phenomena occurring over short timescales can systematically reduce cultural heterogeneity in the long run, and whether offline and online human interactions contribute differently to the process. Theoretical models suggest that short-term collective behavior and long-term cultural diversity are mutually excluding, since they require very different levels of social influence. The latter jointly depends on two factors: the topology of the underlying social network and the overlap between individuals in multidimensional cultural space. However, while the empirical properties of social networks are well understood, little is known about the large-scale organization of real societies in cultural space, so that random input specifications are necessarily used in models. Here we use a large dataset to perform a high-dimensional analysis of the scientific beliefs of thousands of Europeans. We find that inter-opinion correlations determine a nontrivial ultrametric hierarchy of individuals in cultural space, a result unaccessible to one-dimensional analyses and in striking contrast with random assumptions. When empirical data are used as inputs in models, we find that ultrametricity has strong and counterintuitive effects, especially in the extreme case of long-range online-like interactions bypassing social ties. On short time-scales, it strongly facilitates a symmetry-breaking phase transition triggering coordinated social behavior. On long time-scales, it severely suppresses cultural convergence by restricting it within disjoint groups. We therefore find that, remarkably, the empirical distribution of individuals in cultural space appears to optimize the coexistence of short-term collective behavior and long-term cultural diversity, which can be realized simultaneously for the same moderate level of mutual influence

Image-based Recommendations on Styles and Substitutes

Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.Comment: 11 pages, 10 figures, SIGIR 201

Dispatcher3 D4.2 - Prototype package (first release) - User manual

This deliverable along with deliverable D4.1. Technical documentation first release consists of the release of the first prototype of Dispatcher3. The release consists of the binaries and Docker version of the prototype (sent to the Topic Manager). The first release prototype package consists of a set on individual machine learning models which can be executed using Jupyter notebooks. It also includes the integration of the outcome of some of these individual models into a visualisation which would be part of the advice generator to provide high-level information to the end users. All models described in the Deliverable D4.1 will be available and executable in this release. Data required to run the models (with some examples) are also provided. If data are public raw sample values are provided, otherwise pre-computed features are delivered so that the models can be run on individual flight examples. The prototypes can be run using local data (provided in the release) or with data stored in cloud storage (Amazon Web Services (AWS)). This deliverable serves as a manual for the execution of the first release prototype software

One size does not fit all - Translating knowledge to bridge the gaps to diversity and inclusion of surgical teams

Diverse teams have proven their ability to reach superior performance and improve patients' outcomes. Nevertheless, differences in race, gender, age, nationality, skills, education, and experience act as powerful barriers to diversity and inclusion, which negatively impacts multiple healthcare organizations and limit the potential outcome of diverse teams. Knowledge Translation (KT) can help to bridge the gaps among all the various individuals involved, whether they be members of the surgical team or surgical patients

Effective Free Energy for Individual Dynamics

Physics and economics are two disciplines that share the common challenge of linking microscopic and macroscopic behaviors. However, while physics is based on collective dynamics, economics is based on individual choices. This conceptual difference is one of the main obstacles one has to overcome in order to characterize analytically economic models. In this paper, we build both on statistical mechanics and the game theory notion of Potential Function to introduce a rigorous generalization of the physicist's free energy, which includes individual dynamics. Our approach paves the way to analytical treatments of a wide range of socio-economic models and might bring new insights into them. As first examples, we derive solutions for a congestion model and a residential segregation model.Comment: 8 pages, 2 figures, presented at the ECCS'10 conferenc

Clinical Case Management for Patients with Schizophrenia with High Care Needs

The aim of this study is to establish the effectiveness of a clinical case management (CM) programme compared to a standard treatment programme (STP) in patients with schizophrenia. Patients for the CM programme were consecutively selected among patients in the STP with schizophrenia who had poor functioning. Seventy-five patients were admitted to the CM programme and were matched to 75 patients in the STP. Patients were evaluated at baseline and at 1 year follow-up. At baseline, patients in the CM programme showed lower levels of clinical and psychosocial functioning and more care needs than patients in the STP. Both treatment programmes were effective in maintaining contact with services but the CM programme did not show advantages over the STP on outcomes. Differences between groups at baseline may be masking the effects of CM at one year follow-up. A longer follow-up may be required to evaluate the real CM practices effect