Predictors of Treatment with Duloxetine or Venlafaxine XR among Adult Patients Treated for Depression in Primary Care Practices in the United Kingdom

Background. Knowledge about real-world use of duloxetine and venlafaxine XR to treat depression in the UK is limited. Aims. To identify predictors of duloxetine or venlafaxine XR initiation. Method. Adult depressed patients who initiated duloxetine or venlafaxine XR between January 1, 2006 and September 30, 2007 were identified in the UK's General Practice Research Database. Demographic and clinical predictors of treatment initiation with duloxetine and venlafaxine XR were identified using logistic regression. Results. Patients initiating duloxetine (n = 909) were 4 years older than venlafaxine XR recipients (n = 1286). Older age, preexisting unexplained pain, respiratory disease, and pre-period use of anticonvulsants, opioids, and antihyperlipidemics were associated with increased odds of initiating duloxetine compared to venlafaxine XR. Pre-period anxiety disorder was associated with decreased odds of receiving duloxetine. Conclusion. Initial treatment choice with duloxetine versus venlafaxine XR was primarily driven by patient-specific mental and medical health characteristics. General practitioners in the UK favor duloxetine over venlafaxine XR when pain conditions coexist with depression

Simulation of Underwater Explosions Initiated by High-Pressure Gas Bubbles of Various Initial Shapes

UNDerwater EXplosions (UNDEXs) are widely used in many areas of applied engineering including oil production and warship protection. However, the three-dimensional computations of UNDEXs, especially for explosives with complex initial shapes are still lacking, which is mainly due to the difficulty in capturing the multi-medium interface with high pressure ratio. In this study, we conducted a series of three-dimensional numerical simulations of UNDEXs with different initial shapes of a high-pressure gas bubble surrounded with water, to investigate the dynamics of the explosion caused by the shape change of the gas bubble. The movement of the interface was traced with the level-set method, and the conditions at the gas–water interface were treated using the Real Ghost Fluid Method (RGFM). As a result, the temporal evolution of the pressure field during the explosion and the pressure exerted at the boundaries of the computational domain in each simulation scenario were obtained. It was found that an initial shock wave is generated by the explosion and transmitted in the water, leading to an increase of the pressure and density. Meanwhile, inside the gas bubble, a rarefaction wave is formed, causing a pressure drop of the explosive gas. The results also show that if the initial shape of the bubble filled with the explosive gas is simple (e.g., spherical, cylindrical, cuboidal), the peak pressure of the shock wave is dominated by the cross-sectional area of the initial bubble along each direction. In addition, the duration of the high pressure phase of the shock wave is dictated by the thickness of the bubble. Moreover, the simulation of a bubble with an initially bullet-like shape revealed that this specific shape enables a concentration of the energy in a well-defined direction. The peak of the pressure generated by the gas bubble of this more complex shape is approximately twice than that of the other scenarios. However, the high pressure was found to drop more rapidly than that of the other cases, which might be attributed to the comparably small thickness of the initial bubble

Derivation of the reduced reaction mechanisms of ozone depletion events in the Arctic spring by using concentration sensitivity analysis and principal component analysis

The ozone depletion events (ODEs) in the springtime Arctic have been investigated since the 1980s. It is found that the depletion of ozone is highly associated with an auto-catalytic reaction cycle, which involves mostly the bromine-containing compounds. Moreover, bromide stored in various substrates in the Arctic such as the underlying surface covered by ice and snow can be also activated by the absorbed HOBr. Subsequently, this leads to an explosive increase of the bromine amount in the troposphere, which is called the "bromine explosion mechanism". In the present study, a reaction scheme representing the chemistry of ozone depletion and halogen release is processed with two different mechanism reduction approaches, namely, the concentration sensitivity analysis and the principal component analysis. In the concentration sensitivity analysis, the interdependence of the mixing ratios of ozone and principal bromine species on the rate of each reaction in the ODE mechanism is identified. Furthermore, the most influential reactions in different time periods of ODEs are also revealed. By removing 11 reactions with the maximum absolute values of sensitivities lower than 10 %, a reduced reaction mechanism of ODEs is derived. The onsets of each time period of ODEs in simulations using the original reaction mechanism and the reduced reaction mechanism are identical while the maximum deviation of the mixing ratio of principal bromine species between different mechanisms is found to be less than 1 %. By performing the principal component analysis on an array of the sensitivity matrices, the dependence of a particular species concentration on a combination of the reaction rates in the mechanism is revealed. Redundant reactions are indicated by principal components corresponding to small eigenvalues and insignificant elements in principal components with large eigenvalues. Through this investigation, aside from the 11 reactions identified as unimportant in the concentration sensitivity analysis, additionally nine reactions were indicated to contribute only little to the total response of the system. Thus, they can be eliminated from the original reaction scheme. The results computed by applying the reduced reaction mechanism derived after the principal component analysis agree well with those by using the original reaction scheme. The maximum deviation of the mixing ratio of principal bromine species is found to be less than 10 %, which is guaranteed by the selection criterion adopted in the simplification process. Moreover, it is shown in the principal component analysis that O(1D) in the mechanism of ODEs is in quasi-steady state, which enables a following simplification of the reduced reaction mechanism obtained in the present study

Sensitivity of the Reaction Mechanism of the Ozone Depletion Events during the Arctic Spring on the Initial Atmospheric Composition of the Troposphere

Ozone depletion events (ODEs) during the Arctic spring have been investigated since the 1980s. It was found that the depletion of ozone is highly associated with the release of halogens, especially bromine containing compounds. These compounds originate from various substrates such as the ice/snow-covered surfaces in Arctic. In the present study, the dependence of the mixing ratios of ozone and principal bromine species during ODEs on the initial composition of the Arctic atmospheric boundary layer was investigated by using a concentration sensitivity analysis. This analysis was performed by implementing a reaction mechanism representing the ozone depletion and halogen release in the box model KINAL (KInetic aNALysis of reaction mechanics). The ratios between the relative change of the mixing ratios of particular species such as ozone and the variation in the initial concentration of each atmospheric component were calculated, which indicate the relative importance of each initial species in the chemical kinetic system. The results of the computations show that the impact of various chemical species is different for ozone and bromine containing compounds during the depletion of ozone. It was found that CH3CHO critically controls the time scale of the complete removal of ozone. However, the rate of the ozone loss and the maximum values of bromine species are only slightly influenced by the initial value of CH3CHO. In addition, according to the concentration sensitivity analysis, the reduction of initial Br2 was found to cause a significant retardant of the ODE while the initial mixing ratio of HBr exerts minor influence on both ozone and bromine species. In addition, it is also interesting to note that the increase of C2H2 would significantly raise the amount of HOBr and Br in the atmosphere while the ozone depletion is hardly changed

Continuous Similarity Learning with Shared Neural Semantic Representation for Joint Event Detection and Evolution

In the era of the rapid development of today’s Internet, people often feel overwhelmed by vast official news streams or unofficial self-media tweets. To help people obtain the news topics they care about, there is a growing need for systems that can extract important events from this amount of data and construct the evolution procedure of events logically into a story. Most existing methods treat event detection and evolution as two independent subtasks under an integrated pipeline setting. However, the interdependence between these two subtasks is often ignored, which leads to a biased propagation. Besides, due to the limitations of news documents’ semantic representation, the performance of event detection and evolution is still limited. To tackle these problems, we propose a Joint Event Detection and Evolution (JEDE) model, to detect events and discover the event evolution relationships from news streams in this paper. Specifically, the proposed JEDE model is built upon the Siamese network, which first introduces the bidirectional GRU attention network to learn the vector-based semantic representation for news documents shared across two subtask networks. Then, two continuous similarity metrics are learned using stacked neural networks to judge whether two news documents are related to the same event or two events are related to the same story. Furthermore, due to the limited available dataset with ground truths, we make efforts to construct a new dataset, named EDENS, which contains valid labels of events and stories. The experimental results on this newly created dataset demonstrate that, thanks to the shared representation and joint training, the proposed model consistently achieves significant improvements over the baseline methods