Increasing the Reliability of Adaptive Quadrature Using Explicit Interpolants

We present two new adaptive quadrature routines. Both routines differ from previously published algorithms in many aspects, most significantly in how they represent the integrand, how they treat non-numerical values of the integrand, how they deal with improper divergent integrals and how they estimate the integration error. The main focus of these improvements is to increase the reliability of the algorithms without significantly impacting their efficiency. Both algorithms are implemented in Matlab and tested using both the "families" suggested by Lyness and Kaganove and the battery test used by Gander and Gautschi and Kahaner. They are shown to be more reliable, albeit in some cases less efficient, than other commonly-used adaptive integrators.Comment: 32 pages, submitted to ACM Transactions on Mathematical Softwar

The POINT-AGAPE survey II: An Unrestricted Search for Microlensing Events towards M31

An automated search is carried out for microlensing events using a catalogue of 44554 variable superpixel lightcurves derived from our three-year monitoring program of M31. Each step of our candidate selection is objective and reproducible by a computer. Our search is unrestricted, in the sense that it has no explicit timescale cut. So, it must overcome the awkward problem of distinguishing long-timescale microlensing events from long-period stellar variables. The basis of the selection algorithm is the fitting of the superpixel lightcurves to two different theoretical models, using variable star and blended microlensing templates. Only if microlensing is preferred is an event retained as a possible candidate. Further cuts are made with regard to (i) sampling, (ii) goodness of fit of the peak to a Paczynski curve, (iii) consistency of the microlensing hypothesis with the absence of a resolved source, (iv) achromaticity, (v) position in the colour-magnitude diagram and (vi) signal-to-noise ratio. Our results are reported in terms of first-level candidates, which are the most trustworthy, and second-level candidates, which are possible microlensing but have lower signal-to-noise and are more questionable. The pipeline leaves just 3 first-level candidates, all of which have very short full-width half-maximum timescale (<5 days) and 3 second-level candidates, which have timescales of 31, 36 and 51 days respectively. We also show 16 third-level lightcurves, as an illustration of the events that just fail the threshold for designation as microlensing candidates. They are almost certainly mainly variable stars. Two of the 3 first-level candidates correspond to known events (PA 00-S3 and PA 00-S4) already reported by the POINT-AGAPE project. The remaining first-level candidate is new.Comment: 22 pages, 18 figures, MNRAS, to appea

First microlensing candidate towards M31 from the Nainital Microlensing Survey

We report our first microlensing candidate NMS-E1 towards M31 from the data accumulated during the four years of Nainital Microlensing Survey. Cousin R and I band observations of ~13'x13' field in the direction of M31 have been carried out since 1998 and data is analysed using the pixel technique proposed by the AGAPE collaboration. NMS-E1 lies in the disk of M31 at \alpha = 0:43:33.3 and \delta = +41:06:44, about 15.5 arcmin to the South-East direction of the center of M31. The degenerate Paczy\'{n}ski fit gives a half intensity duration of ~59 days. The photometric analysis of the candidate shows that it reached R~20.1 mag at the time of maximum brightness and the colour of the source star was estimated to be (R-I)_0 ~ 1.1 mag. The microlensing candidate is blended by red variable stars; consequently the light curves do not strictly follow the characteristic Paczy\'{n}ski shape and achromatic nature. However its long period monitoring and similar behaviour in R and I bands supports its microlensing nature.Comment: no changes except typos corrected, to appear in A&

Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model.

The aim of this study was to identify a combination of early predictive symptoms/sensations attributable to primary lung cancer (LC). An interactive e-questionnaire comprised of pre-diagnostic descriptors of first symptoms/sensations was administered to patients referred for suspected LC. Respondents were included in the present analysis only if they later received a primary LC diagnosis or had no cancer; and inclusion of each descriptor required ≥4 observations. Fully-completed data from 506/670 individuals later diagnosed with primary LC (n = 311) or no cancer (n = 195) were modelled with orthogonal projections to latent structures (OPLS). After analysing 145/285 descriptors, meeting inclusion criteria, through randomised seven-fold cross-validation (six-fold training set: n = 433; test set: n = 73), 63 provided best LC prediction. The most-significant LC-positive descriptors included a cough that varied over the day, back pain/aches/discomfort, early satiety, appetite loss, and having less strength. Upon combining the descriptors with the background variables current smoking, a cold/flu or pneumonia within the past two years, female sex, older age, a history of COPD (positive LC-association); antibiotics within the past two years, and a history of pneumonia (negative LC-association); the resulting 70-variable model had accurate cross-validated test set performance: area under the ROC curve = 0.767 (descriptors only: 0.736/background predictors only: 0.652), sensitivity = 84.8% (73.9/76.1%, respectively), specificity = 55.6% (66.7/51.9%, respectively). In conclusion, accurate prediction of LC was found through 63 early symptoms/sensations and seven background factors. Further research and precision in this model may lead to a tool for referral and LC diagnostic decision-making

Variation in carbon footprint of milk due to management differences between Swedish dairy farms

To identify mitigation options to reduce greenhouse gas (GHG) emissions from milk production (i.e. the carbon footprint (CF) of milk), this study examined the variation in GHG emissions among dairy farms using data from previous CF studies on Swedish milk. Variation between farms in these production data, which were found to have a strong influence on milk CF were obtained from existing databases of e.g. 1051 dairy farms in Sweden in 2005. Monte Carlo analysis was used to analyse the impact of variations in seven important parameters on milk CF concerning milk yield (energy corrected milk (ECM) produced and delivered), feed dry matter intake (DMI), enteric methane emissions, N content in feed DMI, N-fertiliser rate and diesel used on farm. The largest between farm variation among the analysed production data were N-fertiliser rate (kg/ha) and diesel used (l/ha) on farm (coefficient of variation (CV) 31-38%). For the parameters concerning milk yield and feed DMI the CV was approx. 11 and 8%, respectively. The smallest variation in production data was found for N content in feed DMI. According to the Monte Carlo analysis, these variations in production data led to a variation in milk CF of between 0.94 and 1.33 kg CO2 equivalents (CO2e) per kg ECM, with an average value of 1.13 kg/CO2e kg ECM. We consider that this variation of ±17% that was found based on the used farm data would be even greater if all Swedish dairy farms were included, as the sample of farms in this study was not totally unbiased. The variation identified in milk CF indicates that a potential exists to reduce GHG emissions from milk production on both national and farm level through changes in management. As milk yield and feed DMI are two of the most influential parameters for milk CF, feed conversion efficiency (i.e. units ECM produced per unit DMI) can be used as a rough key performance indicator for predicting CF reductions. However, it must be borne in mind that feeds have different CF due to where and how they are produced

Increase in mast cells and hyaluronic acid correlates to radiation-induced damage and loss of serous acinar cells in salivary glands: the parotid and submandibular glands differ in radiation sensitivity.

The detailed mechanisms which can explain the inherent radiosensitivity of salivary glands remain to be elucidated. Although DNA is the most plausible critical target for the lethal effects of irradiation, interactions with other constituents, such as cell membrane and neuropeptides, have been suggested to cause important physiological changes. Moreover, mast cells seem to be closely linked to radiation-induced pneumonitis. Therefore, in the present study the effects of fractionated irradiation on salivary glands have been assessed with special regard to the appearance of mast cells and its correlation with damage to gland parenchyma. Sprague-Dawley strain rats were unilaterally irradiated to the head and neck with the salivary glands within the radiation field. The irradiation was delivered once daily for 5 days to a total dose of 20, 35 and 45 Gy. The contralateral parotid and submandibular glands served as intra-animal controls and parallel analysis of glands was performed 2, 4, 10 or 180 days following the last radiation treatment. Morphological analysis revealed no obvious changes up to 10 days after the irradiation. At 180 days a radiation dose-dependent loss of gland parenchyma was seen, especially with regard to serious acinar cells in parotid gland and acinar cells and serous CGT (convoluted granular tubule) cells in the submandibular gland. These changes displayed a close correlation with a concomitant dose-dependent enhanced density of mast cells and staining for hyaluronic acid. This cell population seems to conform with the features of the connective tissue mast cell type. The parotid seems to be more sensitive to irradiation than the submandibular gland. Thus, the present results further strengthen the role of and the potential interaction of mast cells with radiation-induced tissue injury and alterations in normal tissue integrity