The Swiss heroin trials: testing alternative approaches

Over half a million heroin misusers receive oral methadone maintenance treatment world-wide1 but the maintenance prescription of injectable opioid drugs, like heroin, remains controversial. In 1992 Switzerland began a large scale evaluation of heroin and other injectable opiate prescribing that eventually involved 1035 misusers. 2 3 The results of the evaluation have recently been reported.4 These show that it was feasible to provide heroin by intravenous injection at a clinic, up to three times a day, for seven days a week. This was done while maintaining good drug control, good order, client safety, and staff morale. Patients were stabilised on 500 to 600 mg heroin daily without evidence of increasing tolerance. Retention in treatment was 89% at six months and 69% at 18 months.4 The self reported use of non-prescribed heroin fell signifianctly, but other drug use was minimally affected. The death rate was 1% per year, and there were no deaths from overdose among participants . . . [Full text of this article]

Radiation use efficiency increases when the diffuse component of incident radiation is enhanced under shade

Theoretical analyses have shown the radiation use efficiency of maize, soybean, and peanut to increase with a decrease in the level of incident radiation and an increase in the proportion of diffuse radiation. This study compared the growth and radiation use efficiency of Panicum maximum cv. Petrie (green panic) and Bothriochloa insculpta cv. Bisset (creeping bluegrass) beneath shading treatments (birdguard and solarweave shadecloths) with that in full sunlight. A level of incident radiation reduced by 25% under birdguard shadecloth decreased final yield and final leaf area index, but increased canopy leaf nitrogen concentration and radiation use efficiency (19-14%) (compared with the full sun treatment). A similar level of reduced incident radiation under solarweave shadecloth (which provided an increased proportion of diffuse radiation), increased final yield and radiation use efficiency (46-50%). An understanding of the effects of composition of incident radiation on radiation use efficiency of tropical grasses enables more accurate estimation of potential pasture growth in shaded environments. It also has impact upon crop production in glasshouses and greenhouses.

Formal derivation of finite state machines for class testing

Previous work on generating state machines for the purpose of class testing has not been formally based. There has also been work on deriving state machines from formal specifications for testing non-object-oriented software. We build on this work by presenting a method for deriving a state machine for testing purposes from a formal specification of the class under test. We also show how the resulting state machine can be used as the basis for a test suite developed and executed using an existing framework for class testing. To derive the state machine, we identify the states and possible interactions of the operations of the class under test. The Test Template Framework is used to formally derive the states from the Object-Z specification of the class under test. The transitions of the finite state machine are calculated from the derived states and the class's operations. The formally derived finite state machine is transformed to a ClassBench testgraph, which is used as input to the ClassBench framework to test a C++ implementation of the class. The method is illustrated using a simple bounded queue example.

Weathering of hydrocarbons in mangrove sediments: testing the effects of using dispersants to treat oil spills

This field study was a combined chemical and biological investigation of the relative effects of using dispersants to treat oil spills impacting mangrove habitats. The aim of the chemistry was to determine whether dispersant affected the short- or long-term composition of a medium range crude oil (Gippsland) stranded in a tropical mangrove environment in Queensland, Australia. Sediment cores from three replicate plots of each treatment (oil only and oil plus dispersant) were analyzed for total hydrocarbons and for individual molecular markers (alkanes, aromatics, triterpanes, and steranes). Sediments were collected at 2 days, then 1, 7, 13 and 22 months post-spill. Over this time, oil in the six treated plots decreased exponentially from 36.6 +/- 16.5 to 1.2 +/- 0.8 mg/g dry wt. There was no statistical difference in initial oil concentrations, penetration of oil to depth, or in the rates of oil dissipation between oiled or dispersed oil plots. At 13 months, alkanes were >50% degraded, aromatics were similar to 30% degraded based upon ratios of labile to resistant markers. However, there was no change in the triterpane or sterane biomarker signatures of the retained oil. This is of general forensic interest for pollution events. The predominant removal processes were evaporation (less than or equal to 27%) and dissolution (greater than or equal to 56%), with a lag-phase of 1 month before the start of significant microbial degradation (less than or equal to 7%). The most resistant fraction of the oil that remained after 7 months (the higher molecular weight hydrocarbons) correlated with the initial total organic carbon content of the soil. Removal rate in the Queensland mangroves was significantly faster than that observed in the Caribbean and was related to tidal flushing. (C) 1999 Elsevier Science Ltd. All rights reserved.

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.

Deconvolving the delta O-18 seawater component from subseasonal coral delta O-18 and Sr/Ca at Rarotonga in the southwestern subtropical Pacific for the period 1726 to 1997

To reconstruct oceanographic variations in the subtropical South Pacific, 271-year long subseasonal time series of Sr/Ca and delta(18)O were generated from a coral growing at Rarotonga (21.5degreesS, 159.5degreesW). In this case, coral Sr/Ca appears to be an excellent proxy for sea surface temperature (SST) and coral delta(18)O is a function of both SST and seawater delta(18)O composition (delta(18)O(sw)). Here, we focus on extracting the delta(18)O(sw) signal from these proxy records. A method is presented assuming that coral Sr/Ca is solely a function of SST and that coral delta(18)O is a function of both SST and delta(18)O(sw). This method separates the effects of delta(18)O(sw) from SST by breaking the instantaneous changes of coral delta(18)O into separate contributions by instantaneous SST and delta(18)O(sw) changes, respectively. The results show that on average delta(18)O(sw) at Rarotonga explains similar to39% of the variance in delta(18)O and that variations in SST explains the remaining similar to61% of delta(18)O variance. Reconstructed delta(18)O(sw) shows systematic increases in summer months (December-February) consistent with the regional pattern of variations in precipitation and evaporation. The delta(18)O(sw) also shows a positive linear correlation with satellite-derived estimated salinity for the period 1980 to 1997 (r = 0.72). This linear correlation between reconstructed delta(18)O(sw) and salinity makes it possible to use the reconstructed delta(18)O(sw) to estimate the past interannual and decadal salinity changes in this region. Comparisons of coral delta(18)O and delta(18)O(sw) at Rarotonga with the Pacific decadal oscillation index suggest that the decadal and interdecadal salinity and SST variability at Rarotonga appears to be related to basin-scale decadal variability in the Pacific. Copyright (C) 2002 Elsevier Science Ltd.

Impact of on-site testing for maternal syphilis on treatment delays, treatment rates, and perinatal mortality in rural South Africa: a randomised controlled trial

Background: Syphilis remains a significant cause of preventable perinatal death in developing countries with many women remaining untested and thus untreated. Syphilis testing in the clinic (on-site testing) may be a useful strategy to overcome this. We studied the impact of on-site syphilis testing on treatment delays and rates, and perinatal mortality. Methods: We conducted a cluster randomised controlled trial among seven pairs of primary healthcare clinics in rural South Africa, comparing on-site testing complemented by laboratory confirmation versus laboratory testing alone. Intervention clinics used the on-site test conducted by primary care nurses, with results and treatment available within an hour. Control clinics sent blood samples to the provincial laboratory, with results returned 2 weeks later. Results: Of 7134 women seeking antenatal care with available test results, 793 (11.1%) tested positive for syphilis. Women at intervention clinics completed treatment 16 days sooner on average (95% confidence interval: 11 to 21), though there was no significant difference in the proportion receiving adequate treatment at intervention (64%) and control (69%) clinics. There was also no significant difference in the proportion experiencing perinatal loss (3.3% v 5.1%; adjusted risk difference: -0.9%; 95% Cl -4.4 to 2.7). Conclusions: Despite reducing treatment delays, the addition of on-site syphilis testing to existing laboratory testing services did not lead to higher treatment rates or reduce perinatal mortality. However on-site testing for syphilis may remain an important option for improving antenatal care in settings where laboratory facilities are not available.

Testing hypotheses about the relationship between cannabis use and psychosis

Aim: To model the impact of rising rates of cannabis use on the incidence and prevalence of psychosis under four hypotheses about the relationship between cannabis use and psychosis. Methods: The study modelled the effects on the prevalence of schizophrenia over the lifespan of cannabis in eight birth cohorts: 1940-1944, 1945-1949, 1950-1954, 1955-1959, 1960-1964, 1965-1969, 1970-1974, 1975-1979. It derived predictions as to the number of cases of schizophrenia that would be observed in these birth cohorts, given the following four hypotheses: (1) that there is a causal relationship between cannabis use and schizophrenia; (2) that cannabis use precipitates schizophrenia in vulnerable persons; (3) that cannabis use exacerbates schizophrenia; and (4) that persons with schizophrenia are more liable to become regular cannabis users. Results: There was a steep rise in the prevalence of cannabis use in Australia over the past 30 years and a corresponding decrease in the age of initiation of cannabis use. There was no evidence of a significant increase in the incidence of schizophrenia over the past 30 years. Data on trends the age of onset of schizophrenia did not show a clear pattern. Cannabis use among persons with schizophrenia has consistently been found to be more common than in the general population. Conclusions: Cannabis use does not appear to be causally related to the incidence of schizophrenia, but its use may precipitate disorders in persons who are vulnerable to developing psychosis and worsen the course of the disorder among those who have already developed it. (C) 2003 Elsevier Science Ireland Ltd. All rights reserved.

Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.