Service Engagement: Psychopathology, Recovery Style and Treatments

The aim of the present study is to evaluate how recovery style, a set of strategies used by patients to interact with services and therapists, and the severity of psychotic symptoms affect the quality/continuity of taking charge of each patient. 156 psychotic patients at different stages of illness were enrolled. Sociodemographic and clinical data were collected and integration/sealing-Over Scale, Recovery Style Questionnaire and Positive and Negative Syndrome Scale were administered. Patients were distinguished into four groups according to the type of treatment received: clinical package, hospital package, day-care package, and residential package. A positive correlation between the cost of psychiatric performance and psychopathological severity (measured with PANSS scores) was identified. No association emerged between ISOS/RSQ total scores and costs. The sanitary expenditure appears to be linked to positive psychotic symptoms while lower performances are given for the treatment of patients with predominant negative symptoms. Recovery style itself has not a direct influence on the quantity/quality of psychiatric services

Taylor's law in innovation processes

Taylor's law quantifies the scaling properties of the fluctuations of the number of innovations occurring in open systems. Urn based modelling schemes have already proven to be effective in modelling this complex behaviour. Here, we present analytical estimations of Taylor's law exponents in such models, by leveraging on their representation in terms of triangular urn models. We also highlight the correspondence of these models with Poisson-Dirichlet processes and demonstrate how a non-trivial Taylor's law exponent is a kind of universal feature in systems related to human activities. We base this result on the analysis of four collections of data generated by human activity: (i) written language (from a Gutenberg corpus); (ii) a n online music website (Last.fm); (iii) Twitter hashtags; (iv) a on-line collaborative tagging system (Del.icio.us). While Taylor's law observed in the last two datasets agrees with the plain model predictions, we need to introduce a generalization to fully characterize the behaviour of the first two datasets, where temporal correlations are possibly more relevant. We suggest that Taylor's law is a fundamental complement to Zipf's and Heaps' laws in unveiling the complex dynamical processes underlying the evolution of systems featuring innovation.Comment: 17 page

Taylor's law in innovation processes

Taylor's law quantifies the scaling properties of the fluctuations of the number of innovations occurring in open systems. Urn-based modeling schemes have already proven to be effective in modeling this complex behaviour. Here, we present analytical estimations of Taylor's law exponents in such models, by leveraging on their representation in terms of triangular urn models. We also highlight the correspondence of these models with Poisson-Dirichlet processes and demonstrate how a non-trivial Taylor's law exponent is a kind of universal feature in systems related to human activities. We base this result on the analysis of four collections of data generated by human activity: (i) written language (from a Gutenberg corpus); (ii) an online music website (Last. fm); (iii) Twitter hashtags; (iv) an online collaborative tagging system (Del. icio. us). While Taylor's law observed in the last two datasets agrees with the plain model predictions, we need to introduce a generalization to fully characterize the behaviour of the first two datasets, where temporal correlations are possibly more relevant. We suggest that Taylor's law is a fundamental complement to Zipf's and Heaps' laws in unveiling the complex dynamical processes underlying the evolution of systems featuring innovation

Fast wide-volume functional imaging of engineered in vitro brain tissues

The need for in vitro models that mimic the human brain to replace animal testing and allow high-throughput screening has driven scientists to develop new tools that reproduce tissue-like features on a chip. Three-dimensional (3D) in vitro cultures are emerging as an unmatched platform that preserves the complexity of cell-to-cell connections within a tissue, improves cell survival, and boosts neuronal differentiation. In this context, new and flexible imaging approaches are required to monitor the functional states of 3D networks. Herein, we propose an experimental model based on 3D neuronal networks in an alginate hydrogel, a tunable wide-volume imaging approach, and an efficient denoising algorithm to resolve, down to single cell resolution, the 3D activity of hundreds of neurons expressing the calcium sensor GCaMP6s. Furthermore, we implemented a 3D co-culture system mimicking the contiguous interfaces of distinct brain tissues such as the cortical-hippocampal interface. The analysis of the network activity of single and layered neuronal co-cultures revealed cell-type-specific activities and an organization of neuronal subpopulations that changed in the two culture configurations. Overall, our experimental platform represents a simple, powerful and cost-effective platform for developing and monitoring living 3D layered brain tissue on chip structures with high resolution and high throughput

Application of an early warning to detect enteropathies in intensive broiler farming

Remote and wearable sensors can be combined with smart algorithms to continuously monitor a wide range of animal responses linked with stress, health status and welfare. The idea of real time monitoring assumes a simple way to measure variable that can give an early warning for the farmer providing clear and suitable alerts to help them in their routine. The prompt reaction to any change in health, welfare and productive status is the key for the reduction in drugs usage and for the improvement of animal wellbeing. In intensive poultry farms, enteric disorders represent a major health issue; these pathologies could be multifactorial and are a major cause of performances reduction. Monitoring poultry health status takes a key role for management to reduce chemicals/drugs and their costs. Nowadays, the preventive use of antibiotics in intensive farming system is common and this practice could lead to the spreading of drugs in the environment, contributing to the phenomenon of antibiotic resistance. Due to the high priority of this issue, it is of great importance the early detection of any health problem in intensive farming. Precision Livestock Farming, through the combination of cheap technologies and specific algorithms, can provide valuable information for farmers starting from the huge amount of data collected in real time at farm level. This study was aimed to the application of a PLF diagnostic tool, sensible to the variation of volatile organic compounds, to promptly recognize enteric problems in intensive farming, supporting veterinarians and enabling specific treatments in case of disease

Model reduction by separation of variables: A comparison between hierarchical model reduction and proper generalized decomposition

Hierarchical Model reduction and Proper Generalized Decomposition both exploit separation of variables to perform a model reduction. After setting the basics, we exemplify these techniques on some standard elliptic problems to highlight pros and cons of the two procedures, both from a methodological and a numerical viewpoint

Turbulence in the Solar Atmosphere: Manifestations and Diagnostics via Solar Image Processing

Intermittent magnetohydrodynamical turbulence is most likely at work in the magnetized solar atmosphere. As a result, an array of scaling and multi-scaling image-processing techniques can be used to measure the expected self-organization of solar magnetic fields. While these techniques advance our understanding of the physical system at work, it is unclear whether they can be used to predict solar eruptions, thus obtaining a practical significance for space weather. We address part of this problem by focusing on solar active regions and by investigating the usefulness of scaling and multi-scaling image-processing techniques in solar flare prediction. Since solar flares exhibit spatial and temporal intermittency, we suggest that they are the products of instabilities subject to a critical threshold in a turbulent magnetic configuration. The identification of this threshold in scaling and multi-scaling spectra would then contribute meaningfully to the prediction of solar flares. We find that the fractal dimension of solar magnetic fields and their multi-fractal spectrum of generalized correlation dimensions do not have significant predictive ability. The respective multi-fractal structure functions and their inertial-range scaling exponents, however, probably provide some statistical distinguishing features between flaring and non-flaring active regions. More importantly, the temporal evolution of the above scaling exponents in flaring active regions probably shows a distinct behavior starting a few hours prior to a flare and therefore this temporal behavior may be practically useful in flare prediction. The results of this study need to be validated by more comprehensive works over a large number of solar active regions.Comment: 26 pages, 7 figure

Predictors of complications in gynaecological oncological surgery: a prospective multicentre study (UKGOSOC-UK gynaecological oncology surgical outcomes and complications)

Background: There are limited data on surgical outcomes in gynaecological oncology. We report on predictors of complications in a multicentre prospective study. / Methods: Data on surgical procedures and resulting complications were contemporaneously recorded on consented patients in 10 participating UK gynaecological cancer centres. Patients were sent follow-up letters to capture any further complications. Post-operative (Post-op) complications were graded (I–V) in increasing severity using the Clavien-Dindo system. Grade I complications were excluded from the analysis. Univariable and multivariable regression was used to identify predictors of complications using all surgery for intra-operative (Intra-op) and only those with both hospital and patient-reported data for Post-op complications. / Results: Prospective data were available on 2948 major operations undertaken between April 2010 and February 2012. Median age was 62 years, with 35% obese and 20.4% ASA grade ⩾3. Consultant gynaecological oncologists performed 74.3% of operations. Intra-op complications were reported in 139 of 2948 and Grade II–V Post-op complications in 379 of 1462 surgeries. The predictors of risk were different for Intra-op and Post-op complications. For Intra-op complications, previous abdominal surgery, metabolic/endocrine disorders (excluding diabetes), surgical complexity and final diagnosis were significant in univariable and multivariable regression (P<0.05), with diabetes only in multivariable regression (P=0.006). For Post-op complications, age, comorbidity status, diabetes, surgical approach, duration of surgery, and final diagnosis were significant in both univariable and multivariable regression (P<0.05). / Conclusions: This multicentre prospective audit benchmarks the considerable morbidity associated with gynaecological oncology surgery. There are significant patient and surgical factors that influence this risk

Case Report: Invasive Fungal Infection and Daratumumab: A Case Series and Review of Literature

Life expectancy of multiple myeloma (MM) patients has improved in last years due to the advent of anti-CD38 monoclonal antibodies in combination with immunomodulators and proteasome inhibitors. However, morbidity and mortality related to infections remain high and represent a major concern. This paper describes the “real life” risk of invasive fungal infections (IFI) in patients treated with daratumumab-based therapy and reviews the relevant literature. In a series of 75 patients we only observed three cases of fungal pneumonia. Unfortunately, the early signs and symptoms were not specific for fungal infection. Diagnostic imaging, microbiology and patient history, especially previous therapies, are critical in the decision to start antifungal treatment. Recognising the subgroup of MM patients with high risk of IFI can increase the rate of diagnosis, adequate treatment and MM-treatment recovery