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

The Experts Method for the prediction of periodic multivariate time series of high dimension = Il Metodo degli Esperti per la previsione di serie temporali multivariate e periodiche, di dimensione elevata

In questo lavoro viene proposto un metodo, detto Metodo degli Esperti, per predire l\u2019evoluzione di un insieme multivariato e di grosse dimensioni di serie temporali. Il metodo e basato sulla definizione di un insieme di \u201desperti\u201d, ovvero \ub4 di porzioni, di un training set delle serie temporali considerate, che approssimano al meglio i dati che precedono quelli che devono essere previsti. Viene utilizzata una opportuna combinazione di decomposizioni ai valori singolari per filtrare il rumore, e fornire previsioni robuste. Il vantaggio di questo metodo, rispetto ai classici metodi di analisi di serie temporali multivariate, e che esso pu \ub4 o essere applicato \ub4 anche quando l\u2019ordine con cui le serie sono registrate nelle colonne del dataset, viene scambiato di tanto in tanto.We propose a method, called Experts Method, to predict the evolution of a high dimensional multivariate set of time series. The method is based on the definition of a set of \u201dexperts\u201d, which are portions of a training set of the considered time series which best fit the data immediately preceding those to be predicted. A suitable combination of Singular Value Decompositions is used to filter out the noise, and provide robust predictions. The advantage of this method, if compared with classical multivariate time series analysis, is that it can be applied also when the time series column order is reshuffled, from time to time, in the collected dataset

A randomized most powerful test to detect a cheater's action. Applicaton to identification of listeriosis in Lombardy

This article presents a new randomized non-parametric test based on a sample of independent but not identically distributed variables; this test detects if a cheater replaces one of the distributions of the sample with a convex-dominating one. The presented test is the uniformely most powerful, in the sense that it is the most powerful for any change of the cheater. We show that this test may be applied when we have variables with distribution satisfying the monotone likelihood ratio property and we need to check whether a parameter of a variable has been changed. The application we present concerns the detection of epidemics of listeriosis in Lombardy from 2005 to 2011

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

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

A simplified numerical model of coronal energy dissipation based on reduced MHD

A 3D model intermediate between cellular automata (CA) models and the reduced magnetohydrodynamic (RMHD) equations is presented to simulate solar impulsive events generated along a coronal magnetic loop. The model consists of a set of planes distributed along a magnetic loop between which the information propagates through Alfven waves. Statistical properties in terms of power-laws for energies and durations of dissipative events are obtained, and their agreement with X-ray and UV flares observations is discussed. The existence of observational biases is also discussed.Comment: 11 pages, 9 figures Accepted for publication in Astronomy & Astrophysic

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