The abundance of HNCO and its use as a diagnostic of environment

Aims. We aim to investigate the chemistry and gas phase abundance of HNCO and the variation of the HNCO/CS abundance ratio as a diagnostic of the physics and chemistry in regions of massive star formation. Methods. A numerical-chemical model has been developed which self-consistently follows the chemical evolution of a hot core. The model comprises of two distinct stages. The first stage follows the isothermal, modified free-fall collapse of a molecular dark cloud. This is immediately followed by an increase in temperature which represents the switch on of a central massive star and the subsequent evolution of the chemistry in a hot, dense gas cloud (the hot core). During the collapse phase, gas species are allowed to accrete on to grain surfaces where they can participate in further reactions. During the hot core phase surface species thermally desorb back in to the ambient gas and further chemical evolution takes place. For comparison, the chemical network was also used to model a simple dark cloud and photodissociation regions. Results. Our investigation reveals that HNCO is inefficiently formed when only gas-phase formation pathways are considered in the chemical network with reaction rates consistent with existing laboratory data. This is particularly true at low temperatures but also in regions with temperatures up to ~200 K. Using currently measured gas phase reaction rates, obtaining the observed HNCO abundances requires its formation on grain surfaces – similar to other “hot core” species such as CH3OH. However our model shows that the gas phase HNCO in hot cores is not a simple direct product of the evaporation of grain mantles. We also show that the HNCO/CS abundance ratio varies as a function of time in hot cores and can match the range of values observed. This ratio is not unambiguously related to the ambient UV field as been suggested – our results are inconsistent with the hypothesis of Martín et al. (2008, ApJ, 678, 245). In addition, our results show that this ratio is extremely sensitive to the initial sulphur abundance. We find that the ratio grows monotonically with time with an absolute value which scales approximately linearly with the S abundance at early times.

Diagnostic based modelling of radio-frequency driven atmospheric pressure plasmas

Diagnostic based modelling (DBM) actively combines complementary advantages of numerical plasma simulations and relatively simple optical emission spectroscopy (OES). DBM is employed to determine absolute atomic oxygen ground state densities in a helium–oxygen radio-frequency driven atmospheric pressure plasma jet. A comparatively simple one-dimensional simulation yields detailed information on electron properties governing the population dynamics of excited states. Important characteristics of the electron dynamics are found to be largely insensitive to details of the chemical composition and to be in very good agreement with space and phase-resolved OES. Benchmarking the time and space resolved simulation allows us to subsequently derive effective excitation rates as the basis for DBM with simple space and time integrated OES. The population dynamics of the upper O 3p 3P (? = 844 nm) atomic oxygen state is governed by direct electron impact excitation, dissociative excitation, radiation losses and collisional induced quenching. Absolute values for atomic oxygen densities are obtained through tracer comparison with the upper Ar 2p1 (? = 750.4 nm) state. The presented results for the atomic oxygen density show excellent quantitative agreement with independent two-photon laser-induced fluorescence measurements.

Evaluation and outcome of patients with chronic non-productive cough using a comprehensive diagnostic protocol

Background-Asthma, post-nasal drip syndrome (PNDS), and gastrooesophageal reflux (GOR) account for many cases of chronic non-productive cough (CNPC). Each may simultaneously contribute to cough even when clinically silent, and failure to recognise their contribution may lead to unsuccessful treatment.

Methods—Patients (all lifetime non-smokers with normal chest radiographs and spirometric measurements) referred with CNPC persisting for more than three weeks as their sole respiratory symptom underwent histamine challenge, home peak flow measurements, ear, nose and throat (ENT) examination, sinus CT scanning, and 24 hour oesophageal pH monitoring. Treatment was prescribed on the basis of diagnoses informed by investigation results.

RESULTS—Forty three patients (29 women) of mean age 47.5 years (range 18-77) and mean cough duration 67 months (range 2-240) were evaluated. On the basis of a successful response to treatment, a cause for the cough was identified in 35 patients (82%) as follows: cough variant asthma (CVA) (10 cases), PNDS (9 cases), GOR (8cases), and dual aetiologies (8 cases). Histamine challenge correctly predicted CVA in 15 of 17 (88%) positive tests. ENT examination and sinus CT scans each had low positive predictive values for PNDS (10 of 16 (63%) and 12 of 18 (67%) positive cases, respectively), suggesting that upper airways disease frequently co-exists but does not always contribute to cough. When negative, histamine challenge and 24 hour oesophageal pH monitoring effectively ruled out CVA and GOR, respectively, as a cause for cough.

CONCLUSION—This comprehensive approach aids the accurate direction of treatment and, while CVA, PNDS and GOR remain the most important causes of CNPC to consider, a group with no identifiable aetiology remains.

Diagnostic-based modeling on a micro-scale atmospheric-pressure plasma jet

Diagnostic-based modeling (DBM) actively combines complementary advantages of numerical plasma simulations and relatively simple optical emission spectroscopy (OES). DBM is applied to determine spatial absolute atomic oxygen ground-state density profiles in a micro atmospheric-pressure plasma jet operated in He–O2. A 1D fluid model with semi-kinetic treatment of the electrons yields detailed information on the electron dynamics and the corresponding spatio-temporal electron energy distribution function. Benchmarking this time- and space-resolved simulation with phase-resolved OES (PROES) allows subsequent derivation of effective excitation rates as the basis for DBM. The population dynamics of the upper O(3p3P) oxygen state (? = 844 nm) is governed by direct electron impact excitation, dissociative excitation, radiation losses, and collisional induced quenching. Absolute values for atomic oxygen densities are obtained through tracer comparison with the upper Ar(2p1) state (? = 750.4 nm). The resulting spatial profile for the absolute atomic oxygen density shows an excellent quantitative agreement to a density profile obtained by two-photon absorption laser-induced fluorescence spectroscopy.

A Ligand predication tool based on modeling and reasoning with imprecise probabilistic knowledge

Ligand prediction has been driven by a fundamental desire to understand more about how biomolecules recognize their ligands and by the commercial imperative to develop new drugs. Most of the current available software systems are very complex and time-consuming to use. Therefore, developing simple and efficient tools to perform initial screening of interesting compounds is an appealing idea. In this paper, we introduce our tool for very rapid screening for likely ligands (either substrates or inhibitors) based on reasoning with imprecise probabilistic knowledge elicited from past experiments. Probabilistic knowledge is input to the system via a user-friendly interface showing a base compound structure. A prediction of whether a particular compound is a substrate is queried against the acquired probabilistic knowledge base and a probability is returned as an indication of the prediction. This tool will be particularly useful in situations where a number of similar compounds have been screened experimentally, but information is not available for all possible members of that group of compounds. We use two case studies to demonstrate how to use the tool.

A survey of formalisms for representing and reasoning with scientific knowledge

With the rapid growth in the quantity and complexity of scientific knowledge available for scientists, and allied professionals, the problems associated with harnessing this knowledge are well recognized. Some of these problems are a result of the uncertainties and inconsistencies that arise in this knowledge. Other problems arise from heterogeneous and informal formats for this knowledge. To address these problems, developments in the application of knowledge representation and reasoning technologies can allow scientific knowledge to be captured in logic-based formalisms. Using such formalisms, we can undertake reasoning with the uncertainty and inconsistency to allow automated techniques to be used for querying and combining of scientific knowledge. Furthermore, by harnessing background knowledge, the querying and combining tasks can be carried out more intelligently. In this paper, we review some of the significant proposals for formalisms for representing and reasoning with scientific knowledge.

Modeling and reasoning with qualitative comparative clinical knowledge

The number of clinical trials reports is increasing rapidly due to a large number of clinical trials being conducted; it, therefore, raises an urgent need to utilize the clinical knowledge contained in the clinical trials reports. In this paper, we focus on the qualitative knowledge instead of quantitative knowledge. More precisely, we aim to model and reason with the qualitative comparison (QC for short) relations which consider qualitatively how strongly one drug/therapy is preferred to another in a clinical point of view. To this end, first, we formalize the QC relations, introduce the notions of QC language, QC base, and QC profile; second, we propose a set of induction rules for the QC relations and provide grading interpretations for the QC bases and show how to determine whether a QC base is consistent. Furthermore, when a QC base is inconsistent, we analyze how to measure inconsistencies among QC bases, and we propose different approaches to merging multiple QC bases. Finally, a case study on lowering intraocular pressure is conducted to illustrate our approaches.