Improving Knowledge of Risk in Dangerous Goods Transport

In order to increase safety as far as dangerous goods transport is concerned, the DESTINATION project has been developed since 2010 in the framework of the Italy/Switzerland Operational Program for Transfrontier Co-operation 2007-2013. The project was born to satisfy the increasing needs of public bodies to share data on hazardous material land transportation and to develop instruments and methodologies to ensure territorial and environmental protection. The project aims to reach this purpose through the increased knowledge of the vulnerable subjects, people and environment, and of the transport activity itself, by using and defining an architecture of data acquisition based on “On Ground Units” (OGU) and “On Board Units” (OBU). These data will be used as an input for a new information system called GIIS (Global Integrated Information System), which manages a risk analysis model of the land transportation of hazardous materials to assess human and environmental vulnerabilities. The GIIS will provide a more effective management of land planning by providing authorities with the possibility of implementing a rational restriction to vehicles transporting dangerous goods within specific areas

Peripheral T-cell lymphoma unspecified (PTCL-U): a new prognostic model from a retrospective multicentric clinical study

To assess the prognosis of peripheral T-cell lymphoma unspecified, we retrospectively analyzed 385 cases fulfilling the criteria defined by the World Health Organization classification. Factors associated with a worse overall survival (OS) in a univariate analysis were age older than 60 years (P=.0002), equal to or more than 2 extranodal sites (P=.0002), lactic dehydrogenase (LDH) value at normal levels or above (P<.0001), performance status (PS) equal to or more than 2 (Pless than or equal to.0001), stage III or higher (P=.0001), and bone marrow involvement (P=.0001). Multivariate analysis showed that age (relative risk, 1.732; 95% CI, 1.300-2.309; P<.0001), PS (relative risk, 1.719; 95% CI, 1.269-2.327, P<.0001), LDH level (relative risk, 1.905; 95% CI, 1.415-2.564; P<.0001), and bone marrow involvement (relative risk, 1.454; 95% CI, 1.045-2.023; P=.026) were factors independently predictive for survival. Using these 4 variables we constructed a new prognostic model that singled out 4 groups at different risk: group 1, no adverse factors, with 5-year and 10-year OS of 62.3% and 54.9%, respectively; group 2, one factor, with a 5-year and 10-year OS of 52.9% and 38.8%, respectively; group 3, 2 factors, with 5-year and 10-year OS of 32.9% and 18.0%, respectively; group 4,3 or 4 factors, with a 5-year and 10-year OS of 18.3 and 12.6%, respectively (Pless than or equal to.0001; log-rank, 66.79)

Decision making and underperformance in competitive environments: evidence from the National Hockey League

We find evidence of suboptimal decisions leading to underperformance in a policy experiment where two teams of professionals compete in a tournament (National Hockey League shootout) performing a task (penalty shot) sequentially. Before an exogenous policy change, home teams had to perform the task second in the sequence. After the policy change, home teams were given the choice to lead or to follow in the sequence. Home teams should move first only when this is optimal, and this should lead them to winning the tournament more often. We find that after given the choice, home teams most of the time choose to move first in the sequence, and this results in a lower winning frequency for them. Contrary to what economic theory would predict, we find that an expanded choice set can lead to worse outcomes for the agents

Pneumocystis carinii pneumonia in patients with malignant haematological diseases: 10 years' experience of infection in GIMEMA centres.

A retrospective survey was conducted over a 10-year period (1990-99) among 52 haematology divisions in order to evaluate the clinical and laboratory characteristics and outcome of patients with proven Pneumocystis carinii pneumonia (PCP) complicating haematological diseases. The study included 55 patients (18 with non-Hodgkin's lymphoma, 10 with acute lymphoblastic leukaemia, eight with acute myeloid leukaemia, five with chronic myeloid leukaemia, four with chronic lymphocytic leukaemia, four with multiple myeloma, three with myelodys-plastic syndrome, two with myelofibrosis and one with thalassemia) who developed PCP. Among these, 18 (33%) underwent stem cell transplantation; only two received an oral prophylaxis with trimethroprim/sulphamethoxazole. Twelve patients (22%) developed PCP despite protective isolation in a laminar airflow room. The most frequent symptoms were: fever (86%), dyspnoea (78%), non-productive cough (71%), thoracic pain (14%) and chills (5%); a severe hypoxaemia was present in 39 patients (71%). Chest radiography or computerized tomography showed interstitial infiltrates in 34 patients (62%), alveolar infiltrates in 12 patients (22%), and alveolar-interstitial infiltrates in nine patients (16%). Bronchoalveolar lavage was diagnostic in 47/48 patients, induced sputum in 9/18 patients and lung biopsy in 3/8 patients. The diagnosis was made in two patients at autopsy. All patients except one started a specific treatment (52 patients trimethroprim/sulphamethoxazole, one pentamidine and one dapsone). Sixteen patients (29%) died of PCP within 30 d of diagnosis. Multivariate analysis showed that prolonged steroid treatment (P < 0.006) and a radiological picture of diffuse lung involvement (P < 0.003) were negative diagnostic factors

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors

[Abstract] The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order kth (Wk). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the Wk(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated Wk(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation)

Impact of Phosphatic Nutrition on Growth Parameters and Artemisinin Production in Artemisia annua Plants Inoculated or Not with Funneliformis mosseae

Artemisia annua L. is a medicinal plant appreciated for the production of artemisinin, a molecule used for malaria treatment. However, the natural concentration of artemisinin in planta is low. Plant nutrition, in particular phosphorus, and arbuscular mycorrhizal (AM) fungi can affect both plant biomass and secondary metabolite production. In this work, A. annua plants were inoculated or not with the AM fungus Funneliformis mosseae BEG12 and cultivated for 2 months in controlled conditions at three different phosphatic (P) concentrations (32, 96, and 288 µM). Plant growth parameters, leaf photosynthetic pigment concentrations, artemisinin production, and mineral uptake were evaluated. The different P levels significantly affected the plant shoot growth, AM fungal colonization, and mineral acquisition. High P levels negatively influenced mycorrhizal colonization. The artemisinin concentration was inversely correlated to the P level in the substrate. The fungus mainly affected root growth and nutrient uptake and significantly lowered leaf artemisinin concentration. In conclusion, P nutrition can influence plant biomass production and the lowest phosphate level led to the highest artemisinin concentration, irrespective of the plant mineral uptake. Plant responses to AM fungi can be modulated by cost–benefit ratios of the mutualistic exchange between the partners and soil nutrient availability

Human leukocyte antigen (Hla) haplotype does not influence the inflammatory pattern of duodenal lymphocytosis linked to irritable bowel syndrome

Background and objectives: Duodenal lymphocytosis (DL) is a condition characterized by enhanced infiltration of intraepithelial lymphocytes (IELs) in the duodenal mucosa, and it can be linked to both gluten-and non-gluten-related diseases, such as irritable bowel syndrome (IBS). Materials and methods: We retrospectively selected patients with DL linked to IBS. Formalin-embedded biopsy samples of the duodenum were collected. CD3 lymphocyte immunohistochemistry was used for IELs. The real-time polymerase chain reaction was used to quantify the amount of mRNA coding for tissue transglutaminase 2 (tTG2), interferon-gamma (IFNγ), toll-like receptor 2 (TLR2), and myeloid differentiation primary response 88 (MyD88). All subjects underwent DQ2-8 haplotype analysis. Controls were represented by subjects with IBS without DL. Results: Thirty-two patients with IBS-DL were retrospectively recruited. Fourteen subjects (43.8%) had a DQ2-8 haplotype. DQ2-8 positive subjects had similar levels compared to negative ones for tTG2, IFNγ, TLR2, and MyD88. Cigarette smoke did not influence molecular expression in our study. Smokers had a statistically higher IELs count than non-smokers (54.2 ± 7.7 vs. 36.0 ± 8.8, p &lt; 0.001). A significant, direct correlation between IELs and duodenal expression of IFNγ was found (r = 0.36, p = 0.04). Conclusions: IBS with DL showed higher expression of inflammatory markers than controls, but DQ2-8 haplotype did not seem to affect their expression. Smoking might increase IELs infiltration

ANN multiscale model of anti-HIV Drugs activity vs AIDS prevalence in the US at county level based on information indices of molecular graphs and social networks

[Abstract] This work is aimed at describing the workflow for a methodology that combines chemoinformatics and pharmacoepidemiology methods and at reporting the first predictive model developed with this methodology. The new model is able to predict complex networks of AIDS prevalence in the US counties, taking into consideration the social determinants and activity/structure of anti-HIV drugs in preclinical assays. We trained different Artificial Neural Networks (ANNs) using as input information indices of social networks and molecular graphs. We used a Shannon information index based on the Gini coefficient to quantify the effect of income inequality in the social network. We obtained the data on AIDS prevalence and the Gini coefficient from the AIDSVu database of Emory University. We also used the Balaban information indices to quantify changes in the chemical structure of anti-HIV drugs. We obtained the data on anti-HIV drug activity and structure (SMILE codes) from the ChEMBL database. Last, we used Box-Jenkins moving average operators to quantify information about the deviations of drugs with respect to data subsets of reference (targets, organisms, experimental parameters, protocols). The best model found was a Linear Neural Network (LNN) with values of Accuracy, Specificity, and Sensitivity above 0.76 and AUROC > 0.80 in training and external validation series. This model generates a complex network of AIDS prevalence in the US at county level with respect to the preclinical activity of anti-HIV drugs in preclinical assays. To train/validate the model and predict the complex network we needed to analyze 43,249 data points including values of AIDS prevalence in 2,310 counties in the US vs ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4,856 protocols, and 10 possible experimental measures.Ministerio de Educación, Cultura y Deportes; AGL2011-30563-C03-0