Incorporating Transportation Network Structure in Spatial Econometric Models of Commodity Flows

We introduce a regression-based gravity model for commodity flows between 35 regions in Austria. We incorporate information regarding the highway network into the spatial connectivity structure of the spatial autoregressive econometric model. We find that our approach produces improved model fit and higher likelihood values. The model accounts for spatial dependence in the origin-destination flows by introducing a spatial connectivity matrix that allows for three types of spatial dependence in the origins to destinations flows. We modify this origin-destination connectivity structure that was introduced by LeSage and Pace (2005) to include information regarding the presence or absence of a major highway/train corridor that passes through the regions. Empirical estimates indicate that the strongest spatial autoregressive effects arise when both origin and destination regions have neighboring regions located on the highway network. Our approach provides a formal spatial econometric methodology that can easily incorporate network connectivity information in spatial autoregressive models.Commodity flows, Spatial autoregression, Bayesian, Maximum likelihood, Spatial connectivity of origin-destination flows

Technical note: Significance and determination of fraction of non-separable particles of impurities in water purification

The method to determine the quantity of non-separable particles in water, together with the calculations for the determination of the conditions of centrifugation under which the quantity of the non-separable particles can be determined, are dealt with in this paper. It is shown that for the determination of the quantity of non-separable particles it is beneficial to replace sedimentation in a gravity field with sedimentation in a centrifugal field. Reduction in the quantity of non-separable particles is characterised by the degree of destabilisation ä, which is proportional to the efficiency of the purification process. The development of separable aggregates is characterised by the degree of aggregation áA which is proportional to the aggregation and separation efficiency of the system. The degree of destabilisation ä corresponds to the collision frequency factor áP in the Smoluchowski equation for the perikinetic coagulation. The degree of aggregation áA corresponds to the collision frequency factor áO in the Smoluchowski equation for orthokinetic coagulation.Keywords: non-separable particles, number of non-separable particles,  degree of destabilisation, degree of aggregation, centrifugation, collision frequency factor

Optimisation of reaction conditions of particle aggregation in water purification – back to basics

Basic types of pollutants in surface waters and methods for their removal are reviewed. Guidelines are provided for selecting the most effective hydrolysing destabilisation reagent (coagulant) for a particular quality surface water, which is polluted with different types of impurities, and optimising the reaction conditions under which its purification takes place. Key words: mineral impurities, natural organic matter (NOM), natural biological materials (NBM), aggregation stability, aggregation destabilisation, optimum dosage, optimised reaction conditions, optimised dosage and optimised reaction pH Water SA Vol.31(1) 2005: 62-7

Internship workplace preferences of final-year medical students at Zagreb University Medical School, Croatia: all roads lead to Zagreb

BACKGROUND: Human resources management in health often encounters problems related to workforce geographical distribution. The aim of this study was to investigate the internship workplace preferences of final-year medical students and the reasons associated with their choices. METHOD: A total of 204 out of 240 final-year medical students at Zagreb University Medical School, Croatia, were surveyed a few months before graduation. We collected data on each student's background, workplace preference, academic performance and emigration preferences. Logistic regression was used to analyse the factors underlying internship workplace preference, classified into two categories: Zagreb versus other areas. RESULTS: Only 39 respondents (19.1%) wanted to obtain internships outside Zagreb, the Croatian capital. Gender and age were not significantly associated with internship workplace preference. A single predictor variable significantly contributed to the logistic regression model: students who believed they would not get the desired specialty more often chose Zagreb as a preferred internship workplace (odds ratio 0.32, 95% CI 0.12–0.86). CONCLUSION: A strong preference for Zagreb as an internship workplace was recorded. Uncertainty about getting the desired specialty was associated with choosing Zagreb as a workplace, possibly due to more extensive and diverse job opportunities

Electron Capture Dissociation Mass Spectrometry of Tyrosine Nitrated Peptides

In vivo protein nitration is associated with many disease conditions that involve oxidative stress and inflammatory response. The modification involves addition of a nitro group at the position ortho to the phenol group of tyrosine to give 3-nitrotyrosine. To understand the mechanisms and consequences of protein nitration, it is necessary to develop methods for identification of nitrotyrosine-containing proteins and localization of the sites of modification.Here, we have investigated the electron capture dissociation (ECD) and collision-induced association (CID) behavior of 3-nitrotyrosine-containing peptides. The presence of nitration did not affect the CID behavior of the peptides. For the doubly-charged peptides, addition of nitration severely inhibited the production of ECD sequence fragments. However, ECD of the triply-charged nitrated peptides resulted in some singly-charged sequence fragments. ECD of the nitrated peptides is characterized by multiple losses of small neutral species including hydroxyl radicals, water and ammonia. The origin of the neutral losses has been investigated by use of activated ion (AI) ECD. Loss of ammonia appears to be the result of non-covalent interactions between the nitro group and protonated lysine side-chains

BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs

<p>Abstract</p> <p>Background</p> <p>The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources. We have developed a statistical text-mining approach (BInary Characteristics Extractor and biomedical Properties Predictor: BICEPP) to help experts screen drugs that may have important clinical characteristics of interest.</p> <p>Results</p> <p>BICEPP first retrieves MEDLINE abstracts containing drug names, then selects tokens that best predict the list of drugs which represents the characteristic of interest. Machine learning is then used to classify drugs using a document frequency-based measure. Evaluation experiments were performed to validate BICEPP's performance on 484 characteristics of 857 drugs, identified from the Australian Medicines Handbook (AMH) and the PharmacoKinetic Interaction Screening (PKIS) database. Stratified cross-validations revealed that BICEPP was able to classify drugs into all 20 major therapeutic classes (100%) and 157 (of 197) minor drug classes (80%) with areas under the receiver operating characteristic curve (AUC) > 0.80. Similarly, AUC > 0.80 could be obtained in the classification of 173 (of 238) adverse events (73%), up to 12 (of 15) groups of clinically significant cytochrome P450 enzyme (CYP) inducers or inhibitors (80%), and up to 11 (of 14) groups of narrow therapeutic index drugs (79%). Interestingly, it was observed that the keywords used to describe a drug characteristic were not necessarily the most predictive ones for the classification task.</p> <p>Conclusions</p> <p>BICEPP has sufficient classification power to automatically distinguish a wide range of clinical properties of drugs. This may be used in pharmacovigilance applications to assist with rapid screening of large drug databases to identify important characteristics for further evaluation.</p

The neural basis of perceived intensity in natural and artificial touch

Electrical stimulation of sensory nerves is a powerful tool for studying neural coding because it can activate neural populations in ways that natural stimulation cannot. Electrical stimulation of the nerve has also been used to restore sensation to patients who have suffered the loss of a limb. We have used long-term implanted electrical interfaces to elucidate the neural basis of perceived intensity in the sense of touch. To this end, we assessed the sensory correlates of neural firing rate and neuronal population recruitment independently by varying two parameters of nerve stimulation: pulse frequency and pulse width. Specifically, two amputees, chronically implanted with peripheral nerve electrodes, performed each of three psychophysical tasks-intensity discrimination, magnitude scaling, and intensity matching-in response to electrical stimulation of their somatosensory nerves. We found that stimulation pulse width and pulse frequency had systematic, cooperative effects on perceived tactile intensity and that the artificial tactile sensations could be reliably matched to skin indentations on the intact limb. We identified a quantity we termed the activation charge rate (ACR), derived from stimulation parameters, that predicted the magnitude of artificial tactile percepts across all testing conditions. On the basis of principles of nerve fiber recruitment, the ACR represents the total population spike count in the activated neural population. Our findings support the hypothesis that population spike count drives the magnitude of tactile percepts and indicate that sensory magnitude can be manipulated systematically by varying a single stimulation quantity

Genetic Determinants of Circulating Sphingolipid Concentrations in European Populations

Sphingolipids have essential roles as structural components of cell membranes and in cell signalling, and disruption of their metabolism causes several diseases, with diverse neurological, psychiatric, and metabolic consequences. Increasingly, variants within a few of the genes that encode enzymes involved in sphingolipid metabolism are being associated with complex disease phenotypes. Direct experimental evidence supports a role of specific sphingolipid species in several common complex chronic disease processes including atherosclerotic plaque formation, myocardial infarction (MI), cardiomyopathy, pancreatic beta-cell failure, insulin resistance, and type 2 diabetes mellitus. Therefore, sphingolipids represent novel and important intermediate phenotypes for genetic analysis, yet little is known about the major genetic variants that influence their circulating levels in the general population. We performed a genome-wide association study (GWAS) between 318,237 single-nucleotide polymorphisms (SNPs) and levels of circulating sphingomyelin (SM), dihydrosphingomyelin (Dih-SM), ceramide (Cer), and glucosylceramide (GluCer) single lipid species (33 traits); and 43 matched metabolite ratios measured in 4,400 subjects from five diverse European populations. Associated variants (32) in five genomic regions were identified with genome-wide significant corrected p-values ranging down to 9.08 x 10(-66). The strongest associations were observed in or near 7 genes functionally involved in ceramide biosynthesis and trafficking: SPTLC3, LASS4, SGPP1, ATP10D, and FADS1-3. Variants in 3 loci (ATP10D, FADS3, and SPTLC3) associate with MI in a series of three German MI studies. An additional 70 variants across 23 candidate genes involved in sphingolipid-metabolizing pathways also demonstrate association (p = 10(-4) or less). Circulating concentrations of several key components in sphingolipid metabolism are thus under strong genetic control, and variants in these loci can be tested for a role in the development of common cardiovascular, metabolic, neurological, and psychiatric diseases