Genetic and environmental bases of childhood antisocial behavior: A multi-informant twin study.

Genetic and environmental influences on childhood antisocial and aggressive behavior (ASB) during childhood were examined in 9- to 10-year-old twins, using a multi-informant approach. The sample (605 families of twins or triplets) was socioeconomically and ethnically diverse, representative of the culturally diverse urban population in Southern California. Measures of ASB included symptom counts for conduct disorder, ratings of aggression, delinquency, and psychopathic traits obtained through child self-reports, teacher, and caregiver ratings. Multivariate analysis revealed a common ASB factor across informants that was strongly heritable (heritability was .96), highlighting the importance of a broad, general measure obtained from multiple sources as a plausible construct for future investigations of specific genetic mechanisms in ASB. The best fitting multivariate model required informant-specific genetic, environmental, and rater effects for variation in observed ASB measures. The results suggest that parents, children, and teachers have only a partly "shared view" and that the additional factors that influence the "rater-specific" view of the child's antisocial behavior vary for different informants. This is the first study to demonstrate strong heritable effects on ASB in ethnically and economically diverse samples. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Impact of scale on the effectiveness of disease control strategies for epidemics with cryptic infection in a dynamical landscape: an example for a crop disease.

We use a spatially explicit, stochastic model to analyse the effectiveness of different scales of local control strategies in containing the long-term, multi-seasonal spread of a crop disease through a dynamically changing population of susceptible crops in which there is cryptic infection. The model distinguishes between susceptible, infested and symptomatic fields. It is motivated by rhizomania disease on sugar beet in the UK as an exemplar of a spatially structured and partially asymptomatic epidemic. Our results show the importance of matching the scales of local control strategies to prevent intensification and regional spread of disease with the inherent temporal and spatial scales of an epidemic. A simple field-scale containment strategy, whereby the susceptible crop is no longer grown on fields showing symptoms, fails for this system with cryptic infection because the locally applied control lags behind the epidemic. A farm-scale strategy, whereby growers respond to the disease status of neighbouring farms by transferring their quota for sugar beet to farmers in regions of reduced risk, succeeds. We conclude that a soil-borne pathogen such as rhizomania could be managed by movement of susceptible crops in the landscape using a strategy that matches the temporal and spatial scales of the epidemic and which take account of risk aversion among growers. We show some parallels and differences in effectiveness between a 'culling' strategy involving crop removal around emerging foci and the local deployment of partially resistant varieties that reduce amplification and transmission of inoculum. Some relationships between the control of plant and livestock diseases are briefly discussed.     

High Sensitivity,Wearable, Piezoresistive Pressure Sensors Based on Irregular Microhump Structures and Its Applications in Body Motion Sensing

A pressure sensor based on irregular microhump patterns has been proposed and developed. The devices show high sensitivity and broad operating pressure regime while comparing with regular micropattern devices. Finite element analysis (FEA) is utilized to confirm the sensing mechanism and predict the performance of the pressure sensor based on the microhump structures. Silicon carbide sandpaper is employed as the mold to develop polydimethylsiloxane (PDMS) microhump patterns with various sizes. The active layer of the piezoresistive pressure sensor is developed by spin coating PEDOT:PSS on top of the patterned PDMS. The devices show an averaged sensitivity as high as 851 kPa^?1, broad operating pressure range (20 kPa), low operating power (100 nW), and fast response speed (6.7 kHz). Owing to their flexible properties, the devices are applied to human body motion sensing and radial artery pulse. These flexible high sensitivity devices show great potential in the next generation of smart sensors for robotics, real‐time health monitoring, and biomedical applications.     

MINING SURPRISING PATTERNS AND THEIR EXPLANATIONS IN CLINICAL DATA

□ We present a new technique for interactively mining patterns and generating explanations by harnessing the expertise of domain experts. Key to the approach is the distinction between what is unexpected from the perspective of the computational data mining process and what is surprising to the domain experts and interesting relative to their needs. We demonstrate the potential of the approach for discovering patterns and generating rich explanations in a clinical domain. Discovering interesting facts in clinical data is a grand challenge, because medical practitioners and clinicians generally have exceptional knowledge in the problem domain in which they work, however, this knowledge is typically difficult to isolate computationally. To identify the desired surprising patterns, we formally record user knowledge and use that knowledge to filter and constrain the output from an objective data mining technique, with the user making the final judgement about whether a rule is surprising. Specifically, we introduce an unexpectedness algorithm based on association rule mining and Bayesian Networks and a ?-explanations technique for explanation generation to identify unexpected patterns. An implemented prototype is successfully demonstrated using a large clinical database recording incidence, prevalance, and outcome of dialysis and kidney transplant patients.     

3D Scene Reconstruction from Multiple Spherical Stereo Pairs

We propose a 3D environment modelling method using multiple pairs of high-resolution spherical images. Spherical images of a scene are captured using a rotating line scan camera. Reconstruction is based on stereo image pairs with a vertical displacement between camera views. A 3D mesh model for each pair of spherical images is reconstructed by stereo matching. For accurate surface reconstruction, we propose a PDE-based disparity estimation method which produces continuous depth fields with sharp depth discontinuities even in occluded and highly textured regions. A full environment model is constructed by fusion of partial reconstruction from spherical stereo pairs at multiple widely spaced locations. To avoid camera calibration steps for all camera locations, we calculate 3D rigid transforms between capture points using feature matching and register all meshes into a unified coordinate system. Finally a complete 3D model of the environment is generated by selecting the most reliable observations among overlapped surface measurements considering surface visibility, orientation and distance from the camera. We analyse the characteristics and behaviour of errors for spherical stereo imaging. Performance of the proposed algorithm is evaluated against ground-truth from the Middlebury stereo test bed and LIDAR scans. Results are also compared with conventional structure-from-motion algorithms. The final composite model is rendered from a wide range of viewpoints with high quality textures.     

GLL parse-tree generation

Backtracking techniques which are often used to extend recursive descent (RD) parsers can have explosive run-times and cannot deal with grammars with left recursion. GLL parsers are fully general, worst-case cubic parsers which have the recursive descent-like property that they are easy to write and to use for grammar debugging. They have the direct relationship with the grammar that an RD parser has. In this paper we give an algorithm for generating GLL parsers which build an SPPF representation of the derivations of the input, complementing our existing GLL recognition algorithm, and we show that such parsers and recognisers are worst-case cubic.     

Nitrate-N determination in leafy vegetables: Study of the effects of cooking and freezing

Nitrate upon reduction to nitrite can cause methaemoglobinaemia or act as precursor in the endogenous formation of carcinogenic nitrosamines. The leafy vegetables are the major vehicle for the entry of nitrate into the human system. The present study was conducted to establish a flow injection analysis (FIA) technique to investigate the nitrate-N contents of four commonly consumed fresh leafy vegetables (Chinese cabbage, celery, lettuce and English cabbage) from market in Fiji. Two extraction techniques (activated carbon and alkaline extraction) were assessed to extract nitrate-N and the activated carbon extraction was preferred over alkaline extraction and applied. The recoveries of spiked nitrate-N in vegetable matrices ranged from 90.40% to 112.80% in activated carbon extraction with an average of 100.62%. The effects of cooking (boiling, baking and frying) and deep-freezing on the nitrate-N contents were also studied. Nitrate contents in selected leafy vegetables were determined by FIA coupled with Greiss protocol involving sulfanilamide and N-(1-naphthyl)ethylenediamine dihydrochloride as color reagents. Nitrate was determined in the linear range from 1.0 to 20.0 mg L⁻¹ with the method detection limit of 0.042 mg L⁻¹ (0.34 mg kg⁻¹). The results of the study show that nitrate contents in fresh leafy vegetables ranged from 1297 to 5658 mg kg⁻¹. Boiling reduces nitrate content by 47–56% whereas frying in Soya bean oil elevates nitrate content by as much as 159–307%. No significant change was observed in nitrate content after baking. The deep-freezing of the selected leafy vegetables shows that nitrate-N content fluctuates slightly from the original nitrate-N values over the seven day period. The FIA throughput was 38 samples h⁻¹.