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Objectives: To investigate knowledge, attitudes, and behavior of nurses working in acute care hospitals regarding clinical risk and patient safety, and to identify predisposing factors. Design: Cross-sectional multicenter study. Methods: The study was conducted in thirteen non-teaching acute public and private hospitals of a region of southern Italy from September through December 2015. A structured self-report questionnaire was administered to clinical nurses working in the hospitals involved. Three multivariate linear and logistic regression models have been constructed: knowledge of the definition of an adverse event; attitude towards risk of making an error while working; and nurses who reported at least one error in the past 12 months. Results: The sample consisted of 484 respondents out of 670 (72.2%) nurses approached. The final multivariable model showed that educational courses about patient safety play a significant role in nurses gaining knowledge of adverse events. In the absence of organization-wide patient safety programs, nurses with low knowledge levels showed a significantly higher perceived risk. Nurses (n=96) who made errors over the past 12 months had discussed them with head nurses (75%) and colleagues (41.7%). Anonymous reporting to the organization was very low, with only 8.3% of nurses who had made an error submitting an anonymous report. Conclusions: Managers should implement multimodal improvement strategies aimed at enabling nurses’ to recognize the critical issues of the system and to increase their reporting, in order to make the organizations safer.

Cancer is the leading cause of death worldwide. Of the various therapeutic approaches, chemotherapy is the most widely used treatment. Among the various side effects associated with this treatment, taste alterations (TAs) have received little attention, even though they have a serious impact on the nutritional aspect and quality of life (QoL) of patients. TAs concern 75% of the patients receiving chemotherapy, and this figure is still considered to be underestimatedbeacuse could be due both to inadequate attention and to the absence of specific subjective tools able to fully evaluate TAs in patients undergoing chemotherapy.

The aim of this paper is to combine evolutionary economics and social network analysis to empirically evaluate the impact of network based policies. The evaluation of these policies is undertaken in the framework of behavioral additionality. In the case study we concentrate on a techological district located in southern Italy to investigate whether the institution of the district has altered the mechanisms by which research collaborations are established amog actors. Results from structural holes analysis and blockmodeling show that indeed a mechanisms of global bridging expanded research collaborations beyond the previous local trajectory.

In this paper we propose a mixed analytical and graphical exploratory strategy based on data archetypes for the exploratory analysis of multivariate data. Our approach is of considerable help in exploring the periphery of the data scatter, exploiting an outward-inward perspective, to highlight small peripheral groups as well as anomalies, outliers and irregularities in the data cloud shape. The strategy is carried out in a comprehensive quantitative programming environment provided by the joint use of the software system R and of the visualization system GGobi. It provides a visualization system involving both static and dynamic graphics based on the so-called multiple views paradigm. The views are organized in a spreadplot and heavily exploit dynamics and interactive statistical graphics. © 2010 Springer-Verlag Berlin Heidelberg.

Performance analysis has become a strategic activity in managing complex systems. The aim of this activity is often benchmarking, that is the comparison among various units on the basis of performance. In statistical terms this corresponds to search for an ordering of units described by a set of p indicators. In this paper we propose a new reduced ordering procedure for multivariate observations based on determining a meaningful direction for the problem in the Euclidean p-dimensional space. Namely, the direction is the one which goes from the “worst” performing units to the “best”, that is the “worst-best” direction. Some proposals for the “worst” and “best” case construction are offered along with the related reduced ordering procedure. Some geometric issues and statistical properties of the proposal are discussed.

A typical problem in marketing research consists of segmenting and clustering products and/or consumers. However, classical cluster analysis and segmentation may fail in the interpretability as they tend to identify average consumers or products, sometimes not well-separated. In this framework, archetypal analysis has been introduced to find extreme segments and well separated typical consumers. On the other hand, we notice that often product attributes and consumer preferences could be more adequately expressed by a range of values in which attributes/preferences may vary. To face these two issues, in this work, we propose an extension of archetypal analysis to the case of interval data, providing a definition of archetypes using the Hausdorff distance, analizing their geometric properties, and offering some appropriate visualization tools. We present also an illustrative example on preference data.

Three-way two mode networks are characterized by a set of n actors and a set of m events in which actors are involved, observed at r different levels (e.g., space or time occasions). They are represented by a three-dimensional relational matrix A=(aijk), where i=1, … , n; j=1,…,m and k=1,…,r. Usually a simultaneous analysis of ways and modes of such a matrix is not explicitly considered. Traditional approaches drop out a mode (e.g., conversion approach of two-mode networks) or a way (e.g., using of trajectories). In this work we propose to analyze the two-mode network as it is. We show how the usual representation through the correspondence analysis does not reproduce correctly relational patterns. At the same time we propose to use Multiple Correspondence Analysis (MCA) to visually explore two-mode network. The adopted metric and the method assumptions better reproduce the structural equivalence pattern present in the two mode network. Finally, we propose the use of Multiple Factorial Analysis, that is useful to generalize MCA to three way matrix, to analyze three way two-mode networks. This method allows to visualize simultaneously the two-mode network observed in different times or spaces.

Background: The management of chronic diseases, particularly in children, requires an integrated physical and psychological approach to both sick children and their family. This is the case of Cystic Fibrosis (CF), a complex genetic chronic disease, where, a comprehensive evaluation of the emotional impact and an effective multidimensional approach are indicated. Aim: This study investigates on parenting stress in children and adolescents with CF and its determinants related to parents, children and the disease severity. Methods: The study involved 34.04% adult males and 65.96% adult females (range 21-55 years) and 47 children with CF, 54.35% males and 45.65% females (range 1-17 years). The data were obtained through a Parenting Stress Index - Short Form (PSI-SF) questionnaire. According to the PSI-SF scoring system, three types of stress were detected: a typical stress pattern (normal), a high stress pattern (increased) and a defensive response, which may be considered as a high stress feature in children which requires monitoring and clinical evaluation. Results: This study shows a significant presence of stress in females (60.23%), of subject married (84.62%), unemployed (69.23%) and with education level such as "middle School" (61.54%). Concerning children of parents with high stress, it resulted most frequent children with one sibling (53.85%). Finally, by univariate analysis, it resulted a significant positive correlation between parenting stress and disease degree of children. Instead by multivariate analysis, we found that the variables: Number of siblings and Birth order were a significant positive and negative predictor of parenting stress respectively. Conclusion: An increased stress level was detected in less than one third of parents of subjects with CF. These data may be related to the psychological support which is part of the routine management of CF care team. However, as children's features seem to act as a determinant of stress more than parental ones, the parental-child dysfunction should be the target for further integrated interventions. Background: The management of chronic diseases, particularly in children, requires an integrated physical and psychological approach to both sick children and their family. This is the case of Cystic Fibrosis (CF), a complex genetic chronic disease, where, a comprehensive evaluation of the emotional impact and an effective multidimensional approach are indicated. Aim: This study investigates on parenting stress in children and adolescents with CF and its determinants related to parents, children and the disease severity. Methods: The study involved 34.04% adult males and 65.96% adult females (range 21-55 years) and 47 children with CF, 54.35% males and 45.65% females (range 1-17 years). The data were obtained through a Parenting Stress Index – Short Form (PSI-SF) questionnaire. According to the PSI-SF scoring system, three types of stress were detected: a typical stress pattern (normal), a high stress pattern (increased) and a defensive response, which may be considered as a high stress feature in children which requires monitoring and clinical evaluation. Results: This study shows a significant presence of stress in females (60.23%), of subject married (84.62%), unemployed (69.23%) and with education level such as “middle School” (61.54%). Concerning children of parents with high stress, it resulted most frequent children with one sibling (53.85%). Finally, by univariate analysis, it resulted a significant positive correlation between parenting stress and disease degree of children. Instead by multivariate analysis, we found that the variables: Number of siblings and Birth order were a significant positive and negative predictor of parenting stress respectively. Conclusion: An increased stress level was detected in less than one third of parents of subjects with CF. These data may be related to the psychological support which is part of the routine management of CF care team. However, as children’s features seem to act as a determinant of stress more than parental ones, the parental-child dysfunction should be the target for further integrated interventions.

Today, nurses play a fundamental role in Public Health. In the last few decades the health profession has seen strong professional growth, in the areas of Clinical practice, Management and Education. In particular, this growth has been also informed by the progress being made in nursing research. In fact, nursing research has contributed to the establishment of support tools to facilitate and evaluate the daily work done by nurses, contributing significantly to the improvement of their professional performance.

Background and Objectives: Game addiction is an emerging problem in public health. A gaming disorder is characterized by a pattern of persistent or recurrent gaming behavior. The behavioral pattern is severe enough to implicate a significant involvement of family, social, educational, professional, or other relationships. Therefore, greater attention needs to be paid to potential addictive behaviors in terms of video games in order to identify both pre-adolescents and adolescents at risk and to provide them with adequate assistance. Materials and Methods: A random sample of 622 students including pre-adolescents and adolescents were enrolled from September 1st to October 31th 2016, and the Game Addiction Scale (GAS) interview was used to identify pathological students with both Monothetic and Polythetic analysis. Results: This study shows the presence of pathological students is equal to 1.93%, with 37.46% and 4.50% obtained with Monothetic and Polythetic analysis (global and partial), respectively. In our sample, the most frequent were students with a gaming time of 1 or 2 h, and students with a day gaming frequency of 1, 2, or 3 times a day. The items with more pathological students were Item 2 (i.e., Tolerance) and 4 (i.e., Withdrawal). Every item was positively correlated with Daily gaming time(hours) and Daily game frequency, excluding Item 4(i.e., Withdrawal). Finally, the Monothetic GAS score was positively correlated with Daily gaming time while the Polythetic Global GAS was positively correlated with Daily game frequency and negatively with Education level, instead, the Polythetic Partial GAS score was positively correlated with only Daily gaming time. Conclusion: Males are pathological gamblers more so than females and spend more time playing video games. An increase in Daily game frequency or Daily gaming time implicates an increase in video game addictions, while an increase in Education level, which generally corresponds to a greater age, implicates a decrease in game addiction. Finally, we observed that the correlations obtained between the Polythetic Partial GAS score with the independent variables such as Age, Gender, Education level, Daily gaming time (hours), and Daily game frequency were analogous to the significant correlations obtained with the Monothetic GAS score, while these correlations were different for the Polythetic Global GAS and the independent variables. These results suggest that the use of the original Polythetic scale should not be neglected.

Neuroblastoma (NB) is an embryonic malignancy of the sympathetic nervous system with heterogeneous biological, morphological, genetic and clinical characteristics. Although genomic studies revealed the specific biological features of NB pathogenesis useful for new therapeutic approaches, the improvement of high-risk (HR)-NB patients overall survival remains unsatisfactory. To further clarify the biological basis of disease aggressiveness, we used whole-exome sequencing to examine the genomic landscape of HR-NB patients at stage M with short survival (SS) and long survival (LS). Only a few genes, including SMARCA4, SMO, ZNF44 and CHD2, were recurrently and specifically mutated in the SS group, confirming the low recurrence of common mutations in this tumor. A systems biology approach revealed that in the two patient groups, mutations occurred in different pathways. Mutated genes (ARHGEF11, CACNA1G, FGF4, PTPRA, PTK2, ANK3, SMO, NTNG2, VCL and NID2) regulate the MAPK pathway associated with the organization of the extracellular matrix, cell motility through PTK2 signaling and matrix metalloproteinase activity. Moreover, we detected mutations in LAMA2, PTK2, LAMA4, and MMP14 genes, impairing MET signaling, in SFI1 and CHD2 involved in centrosome maturation and chromosome remodeling, in AK7 and SPTLC2, which regulate the metabolism of nucleotides and lipoproteins, and in NALCN, SLC12A1, SLC9A9, which are involved in the transport of small molecules. Notably, connected networks of somatically mutated genes specific for SS patients were identified. The detection of mutated genes present at the onset of disease may help to address an early treatment of HR-NB patients using FDA-approved compounds targeting the deregulated pathways.

Prototypes, as Rosch (1973) defined the term in the cognitive sciences field, are ideal exemplars that summarize and represent groups of objects (or categories) and that are “typical" according to their internal resemblance and external dissimilarity vis-à-vis other groups or categories. In line with the cognitive approach, we propose a data-driven procedure for identifying prototypes that is based on archetypal analysis and compositional data analysis. The procedure presented here exploits the properties of archetypes, both in terms of their external dissimilarity in relation to other points in the data set and in terms of their ability to represent the data through compositions in a simplex in which it is possible to cluster all of the observations. The proposed procedure is useful not only for the usual real data points; it may also be used for interval-valued data, functional data, and relational data, and it provides well-separated and clearly profiled prototypes. © 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016.

Archetypal analysis aims at synthesizing single-valued data sets through a few (not necessarily observed) points that are called archetypes, under the constraint that all points can be represented as a convex combination of the archetypes themselves and that the archetypes are a convex combination of the data. In this paper, we extend this methodology to the case of interval-valued data, which represent a special case of set-valued data, where the sets are compact and identified by ordered pairs of values. In addition, we propose to use interval archetypes as a tool in an analysis strategy to explore and mine complex data sets. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 5: 322-335, 2012

In the present work we propose to analyze, through Multiple Factorial Analysis (MFA), the relational structure embedded in collaboration networks observed across time occasions. We show, through a case study, how the solutions provided by the MFA can be interpreted in a suitable way in the relational setting which arises in complex and heterogeneous networks. Valuable information about the strength and typology of the collaboration structure and its evolution can be obtained. As case study, we analyze a Technological District located in South Italy.

Most social networks present complex structures. They can be both multi-modal and multi-relational. In addition, each relationship can be observed across time occasions. Relational data observed in such conditions can be organized into multidimensional arrays and statistical methods from the theory of multiway data analysis may be exploited to reveal the underlying data structure. In this paper, we adopt an exploratory data analysis point of view, and we present a procedure based on multiple factor analysis and multiple correspondence analysis to deal with time-varying two-mode networks. This procedure allows us to create static displays in order to explore network evolutions and to visually analyze the degree of similarity of actor/event network profiles over time while preserving the different statuses of the two modes.

In this paper we propose a method to analyze and synthesize a set of N networks that refer to a common scenario and that are comparable among each other. Examples of this type of data are: a set of collaboration networks, each defined for a different scientific field; or a set of ego networks, where egos belong to a same category; a set of governance networks, etc. For these kind of sets of networks it can be of interest to find a small number of representative networks that can serve as a condensed view of the data set. In a statistical perspective this goal amount to find a small number of networks that are able to typify the network structures starting from the observed ones. In addition, these networks should have a clear and interpretable profile in terms of their most relevant features and their specificity in contrast to the others. Given the set of N networks, we propose to find these representative networks by using the archetypal analysis, yielding what we call Archetypal Networks. The Archetypal Networks can serve to understand the data structure, as benchmarks for the other networks, and are useful also to compare networks among each other. We exemplify the proposed procedure by analyzing a set of 36 governance networks of public structures devoted to provide youth services and referring to 36 different territorial districts in Campania region in Italy. Our results highlight the presence of different network structures that can be interpreted in terms of the governance forms established in literature.

Factorial techniques are widely used in Social Network Analysis to analyze and visualize networks. When the purpose is to represent the relational similarities, simple correspondence analysis is the most frequent used technique. However, in the case of affiliation networks, its use can be criticized because the involved χ 2 distance does not adequately reflect the actual relational patterns. In this paper we perform a simulation study to compare the metric involved in Correspondence Analysis with respect to the one in Multiple Correspondence Analysis. Analytical results and simulation outcomes show that Multiple Correspondence Analysis allows a proper graphical appraisal of the underlying two-mode relational structure.

In this paper we discuss the use of Multiple Correspondence Analysis to analyze and graphically represent two-mode networks, and we propose to apply it in a Greenacre's doubling perspective. We discuss how Multiple Correspondence Analysis: (i) properly takes into account the nature of relational data and the intrinsic asymmetry of actors/events in two-mode networks; (ii) allows a proper graphical appraisal of the underlying relational structure of actors or events; (iii) makes it possible to add actor and event attributes to the analysis in order to improve results interpretation; and (iv) gives different results with respect to the usual Simple Correspondence Analysis. © 2014 Elsevier B.V.

Evidence from economic literature suggests that innovative activities based on extensive interactions between industry, universities and local government can yield high levels of economic performance. In many countries, therefore, steps have been taken at an institutional level to set up innovation networks and, in particular, regional technological districts. Our paper deals with Italian Technological Districts: we aim to analyse the network additionality for territorial innovation determined by district policy. The analysis is based on a priori structural regional characteristics and on Social Network Analysis techniques.

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The paper focuses on territorial innovative networks.The main goal of the analysis is to highlight netwoerk characteristics that can influence networks efficiency in terms of knowledge exchange and diffusion of innovation.

Archetypal analysis is a statistical method aiming at synthesizing a set of multivariate observations through few points not necessarily observed. On the other hand, coding data as interval values allows to include variability and variation in the data itself. This work proposes the use of archetypal analysis for intervalcoded sensory data to synthesize profiling data taking into account assessor panel variability. © Springer-Verlag Berlin Heidelberg 2011.

Symbolic Data Analysis has represented an important innovation in statistics since its first presentation by E. Diday in the late 1980s. Most of the interest has been for the statistical analysis of Symbolic Data that represent complex data structure where variables can assume more than just a single value. Thus, Symbolic Data allow to describe classes of statistical units as a whole. Furthermore, other entities can be defined in the realm of Symbolic data. These entities are the Symbolic objects, defined in terms of the relationships between two different knowledge levels. This article aims at introducing a new type of SO based on the archetypal analysis.

International audience; BACKGROUND: Stomatocytoses are a group of inherited autosomal dominant hemolytic anemias and include overhydrated hereditary stomatocytosis, dehydrated hereditary stomatocytosis, hereditary cryohydrocytosis and familial pseudohyperkalemia. DESIGN AND METHODS: We report a novel variant of hereditary stomatocytosis due to a de novo band 3 mutation (p. G796R-band3 CEINGE) associated with a dyserythropoietic phenotype. Band 3 genomic analysis, measurement at of hematologic parameters and red cell indices and morphological analysis of bone marrow were carried out. We then evaluated the red cell membrane permeability and ion transport systems by functional studies of the patient's erythrocytes and Xenopus oocytes transfected with mutated band 3. We analyzed the red cell membrane tyrosine phosphorylation profile and the membrane association of the tyrosine kinases Syk and Lyn from the Src-family-kinase group, since the activity of the membrane cation transport pathways is related to cyclic phosphorylation-dephosphorylation events. RESULTS: The patient showed mild hemolytic anemia with circulating stomatocytes together with signs of dyserythropoiesis. Her red cells displayed increased Na(+) content with decreased K(+)content and abnormal membrane cation transport activities. Functional characterization of band 3 CEINGE in Xenopus oocytes showed that the mutated band 3 is converted from being an anion exchanger (Cl(-), HCO(3)(-)) to being a cation pathway for Na(+) and K(+). Increased tyrosine phosphorylation of some red cell membrane proteins was observed in diseased erythrocytes. Syk and Lyn membrane association was increased in the patient's red cells compared to in normal controls, indicating perturbation of phospho-signaling pathways involved in cell volume regulation events. CONCLUSIONS: Band 3 CEINGE alters function from that of anion exchange to cation transport, affects the membrane tyrosine phosphorylation profile, in particular of band 3 and stomatin, and its presence during red cell development likely contributes to dyserythropiesis.

Several studies highlight that the labour market entry is strongly influenced by informal relational networks (friendship and kinship). Given that informal networks have a relevant role in job position, the present work deals with the analysis of the relationship between social and occupational segregation in the labour market context. Starting from a panel survey on 506 unemployed people (and informal economy workers) enrolled in the database of the Labour Agency of Scampia in Naples, we define a network structure. In particular, the analysis of the affiliation matrix, where professional profiles related to undeclared works of officially unemployed people are the nodes and professional profiles of their three most important friends are the events, is performed to identify the underlying dimensions of the joint factorial map defined by both actors and events.

Congenital dyserythropoietic anemias (CDAs) are phenotypically and genotypically heterogeneous diseases(1-4). CDA type II (CDAII) is the most frequent CDA. It is characterized by ineffective erythropoiesis and by the presence of bi- and multinucleated erythroblasts in bone marrow, with nuclei of equal size and DNA content, suggesting a cytokinesis disturbance(5). Other features of the peripheral red blood cells are protein and lipid dysglycosylation and endoplasmic reticulum double-membrane remnants(4,6). Development of other hematopoietic lineages is normal. Individuals with CDAII show progressive splenomegaly, gallstones and iron overload potentially with liver cirrhosis or cardiac failure. Here we show that the gene encoding the secretory COPII component SEC23B is mutated in CDAII. Short hairpin RNA (shRNA)-mediated suppression of SEC23B expression recapitulates the cytokinesis defect. Knockdown of zebrafish sec23b also leads to aberrant erythrocyte development. Our results provide in vivo evidence for SEC23B selectivity in erythroid differentiation and show that SEC23A and SEC23B, although highly related paralogous secretory COPII components, are nonredundant in erythrocyte maturation.

Within the regression context, this method begins with the set of exactly fitted coefficients determined from each p-dimensional subset of the sample. Outlying points in this p-dimensional coefficient space correspond to outliers in the original n-dimensional data space. Resampled values are used to detect anomalous data points through a proposed detection rule that avoids masking and swamping and allows multiple outliers to be identified.