Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms

Simultaneous identification and quantitative determination in urine of the more significant metabolites of synthetic cannabinoids JWH-018, JWH-073, JWH-122 and JWH-250 using authentic references and deuterated isotopologues as internal standards

Introduction Synthetic cannabinoids (SC) are substances displaying a high affinity for cannabinoid receptor CB1 and represent the psychoactive agents in herbal mixtures called \u201cSpice\u201d or \u201cK2\u201d which are sold as an incense or smoking material mainly through the Internet. Because of its great abusive potential, several SC are banned in many countries, but despite this, the widespread use of herbal smoking mixtures containing SC may be partially explained by the fact that post-ingestion urines are known to produce negative results in standard toxicological screening methods for cannabis. As a consequence, an increasing number of analytical methods have been developed in forensic and doping control laboratories to enable the detection of illegal intake of these psychoactive substances in human fluids originating from psychiatric patients, emergency units or assessment of fitness to drive. Due to rapid metabolic transformation, the native SC are not usually detectable in urine samples and then the analytical methods must be based on the identification and quantization of their metabolites. Aims The aim of our study is to set-up, using synthesized reference standards, a LC-MS/MS method for routine screening procedures to assess the assumption of JWH-018, JWH-073, JWH 122 and JWH-250, the SC included in Table 1 of narcotic and psychotropic substances banned in Italy. The method gives the simultaneous identification of the three more significant metabolites of each cannabinoid and adequate sensitivity, precision and accuracy are assured by the use of deuterated internal standards. Methods For each of the four cannabinoids were synthesized the three more significant metabolites, the \u3c9- and (\u3c9-1)-hydroxyl and the \u3c9-carboxyl derivatives (\u3c9 position represent the terminal carbon of the N-alkyl side chain) while as internal standards were synthesized the (\u3c9-1)-hydroxyl metabolites trideuterated on the terminal methyl of the side chain. Urine samples were subjected to deconjugation using 30% hydrochloric acid at 90-95\ub0C for 60 min, followed by a solvent extraction procedure with n-hexane-ethyl acetate (9/1 v/v). The LC-MS/MS analysis was performed in positive mode on an API 4000 Triple Quadrupole Mass Spectrometer (AB Sciex) equipped with a 1,8\ub5m Acquity C-18 HSS T3 100 x 2 mm HPLC column (Waters) with isocratic elution (55 % of 10 mM HCOONH4 in water containing 0.1% HCOOH and 45 % of acetonitrile) at 45 \ub0C and at flow rate of 0.4 mL/min. Two transitions in \u2018multiple reaction monitoring\u2019 mode and the retention time have permitted the unambiguous identification of each metabolite which, through the presence of a suitable internal standard, was quantified. Result and discussion All the synthesized compounds were fully characterized with regard to the structure and purity, by 1H,13C NMR and GC-MS (after esterification with CH2N2 for the \u3c9-carboxylic metabolites). For sample treatment, the recovery of the metabolites was evaluated at different pH (1, 3, 5, 9 and 10). The validation of the method was performed testing linearity (0.5-100 ng/mL), reproducibility and accuracy (ranged between -15% and + 15%) at three levels. The method developed was applied to the analysis of urine samples from individuals who have taken SC. This LC-MS/MS method can be used for routine screening of urine specimens from subjects suspected of using \u201cherbal incense\u201d or \u201cSpice\u201d products spiked with the synthetic cannabinoids JWH-018, JWH-073, JWH 122 or JWH-250

New coil concept for endoluminal MR imaging: Initial results in staging of gastric carcinoma in correlation with Histopathology

Our aim was to conduct a prospective study to evaluate staging accuracy of a new coil concept for endoluminal magnetic resonance imaging (MRI) on ex vivo gastric carcinomas. Twenty-eight consecutive patients referred to surgery with a clinically proven primary gastric malignancy were included. Surgical specimens were examined with a foldable and self-expanding loop coil (8-cm diameter) at 1.5 Tesla immediately after total gastrectomy. T1- and T2-weighted and opposed-phase sequences (axial, frontal sections; 3- to 4-mm slice thickness) were acquired. Investigators blinded to any patient information analyzed signal intensity of normal gastric wall, gastric tumor, and lymph nodes. Findings were compared with histopathological staging. On surgical specimens, 2–5 gastric wall layers could be visualized. All gastric tumors (26 carcinomas, two lymphomas) were identified on endoluminal MR data (100%). Overall accuracy for T staging was 75% (18/24); sensitivity to detect serosal involvement was 80% and specificity 89%. N staging correlated in 58% (14/24) with histopathology (N+ versus N−). The endoluminal coil concept is feasible and applicable for an ex vivo setting. Endoluminal MR data provided sufficient detail for gastric wall layer differentiation, and therefore, identification of T stages in gastric carcinoma is possible. Further investigations in in vivo settings should explore the potential of our coil concept for endoluminal MR imaging

Characterization of the Dispersal of Non-Domiciliated Triatoma dimidiata through the Selection of Spatially Explicit Models

Chagas disease is one of the most important neglected diseases in Latin America. Although insecticides have been successfully sprayed to control domiciliated vector populations, this strategy has proven to be ineffective in areas where non-domiciliated vectors immigrating from peridomestic or sylvatic ecotopes can (re-)infest houses. The development of strategies for the control of non-domiciliated vectors has thus been identified by the World Health Organization as a major challenge. Such development primarily requires a description of the spatio-temporal dynamics of infestation by these vectors, and a good understanding of their dispersal. We combined for the first time extensive spatio-temporal data sets describing house infestation dynamics by Triatoma dimidiata inside one village, and spatially explicit population dynamics models. The models fitted and predicted remarkably the observed infestation dynamics. They thus provided both key insights into the dispersal of T. dimidiata in this area, and a suitable mathematical background to evaluate the efficacy of various control strategies. Interestingly, the observed and modelled patterns of infestation suggest that interventions could focus on the periphery of the village, where there is the highest risk of transmission. Such spatial optimization may allow for reducing the cost of control, compensating for repeated interventions necessary for non-domiciliated vectors

Seasonal pattern of peptic ulcer hospitalizations: analysis of the hospital discharge data of the Emilia-Romagna region of Italy

BACKGROUND: Previous studies have reported seasonal variation in peptic ulcer disease (PUD), but few large-scale, population-based studies have been conducted. METHODS: To verify whether a seasonal variation in cases of PUD (either complicated or not complicated) requiring acute hospitalization exists, we assessed the database of hospital admissions of the region Emilia Romagna (RER), Italy, obtained from the Center for Health Statistics, between January 1998 and December 2005. Admissions were categorized by sex, age ( or = 75 yrs), site of PUD lesion (stomach or duodenum), main complication (hemorrhage or perforation), and final outcome (intended as fatal outcome: in-hospital death; nonfatal outcome: patient discharged alive). Temporal patterns in PUD admissions were assessed in two ways, considering a) total counts per single month and season, and b) prevalence proportion, such as the monthly prevalence of PUD admissions divided by the monthly prevalence of total hospital admissions, to assess if the temporal patterns in the raw data might be the consequence of seasonal and annual variations in hospital admissions per se in the region. For statistical analysis, the chi2 test for goodness of fit and inferential chronobiologic method (Cosinor and partial Fourier series) were used. RESULTS: Of the total sample of PUD patients (26,848 [16,795 males, age 65 +/- 16 yrs; 10,053 females, age 72 +/- 15 yrs, p or = 75 yrs of age. There were more cases of duodenal (DU). (89.8%) than gastric ulcer (GU) (3.6%), and there were 1,290 (4.8%) fatal events. Data by season showed a statistically difference with the lowest proportion of PUD hospital admissions in summer (23.3%) (p < 0.001), for total cases and rather all subgroups. Chronobiological analysis identified three major peaks of PUD hospitalizations (September-October, January-February, and April-May) for the whole sample (p = 0.035), and several subgroups, with nadir in July. Finally, analysis of the monthly prevalence proportions yielded a significant (p = 0.025) biphasic pattern with a main peak in August-September-October, and a secondary one in January-February. CONCLUSIONS: A seasonal variation in PUD hospitalization, characterized by three peaks of higher incidence (Autumn, Winter, and Spring) is observed. When data corrected by monthly admission proportions are analyzed, late summer-autumn and winter are confirmed as higher risk periods. The underlying pathophysiologic mechanisms are unknown, and need further studies. In subjects at higher risk, certain periods of the year could deserve an appropriate pharmacological protection to reduce the risk of PUD hospitalization

AgMIP-Wheat multi-model simulations on climate change impact and adaptation for global wheat, SDATA-20-01059

The climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchmarked against a large number of experimental data, including different locations, growing season temperatures, atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate change scenario (RCP8.5, 571 ppm CO2), and 1.5°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (doi: 10.5281/zenodo.4027033).Two scientific publications have been published based on some of these data here

Exploring Statistical and Population Aspects of Network Complexity

The characterization and the definition of the complexity of objects is an important but very difficult problem that attracted much interest in many different fields. In this paper we introduce a new measure, called network diversity score (NDS), which allows us to quantify structural properties of networks. We demonstrate numerically that our diversity score is capable of distinguishing ordered, random and complex networks from each other and, hence, allowing us to categorize networks with respect to their structural complexity. We study 16 additional network complexity measures and find that none of these measures has similar good categorization capabilities. In contrast to many other measures suggested so far aiming for a characterization of the structural complexity of networks, our score is different for a variety of reasons. First, our score is multiplicatively composed of four individual scores, each assessing different structural properties of a network. That means our composite score reflects the structural diversity of a network. Second, our score is defined for a population of networks instead of individual networks. We will show that this removes an unwanted ambiguity, inherently present in measures that are based on single networks. In order to apply our measure practically, we provide a statistical estimator for the diversity score, which is based on a finite number of samples

Network Compression as a Quality Measure for Protein Interaction Networks

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients