Structure,spatio-temporal dynamics and disturbance regime of the mixed beech–silver fir–Norway spruce old-growth forest of Biogradska Gora (Montenegro)

The structure and the spatio-temporal dynamics of the mixed beech–silver fir–Norway spruce old-growth forest of Biogradska Gora (Montenegro) have been analysed at different spatial scales: at the landscape scale, using a high-resolution SPOT5 satellite image and at the stand level with an intensive field survey. This remote-sensing approach has been used to obtain a land cover map in order to define the main vegetation types and to detect the large canopy gaps (>150 m²). The structural characteristics have been delineated in a 50-ha study area in which a regular 120-m grid was superimposed over a 1:10,000 raster map and 30 sampling points have been obtained. The forest is characterized by a high volume of living trees (1029.6 m³ ha^? 1) and coarse woody debris (420.4 m³ ha^? 1) and by small-scale disturbances (individual trees to small groups) with a low incidence of intermediate disturbances (18 forest canopy gaps>150 m² over 1230 ha). The two approaches have proved useful to delineate the spatio-temporal dynamics. The Biogradska Gora forest dynamics are dominated by very small-scale processes, which are partially autogenic and partially caused by allogenic factors. The influence of large-scale or intermediate disturbances has shown to be negligible.     

Towards automatic quantification of the epicardial fat in non-contrasted CT images

In this work, we present a technique to semi-automatically quantify the epicardial fat in non-contrasted computed tomography (CT) images. The epicardial fat is very close to the pericardial fat, being separated only by the pericardium that appears in the image as a very thin line, which is hard to detect. Therefore, an algorithm that uses the anatomy of the heart was developed to detect the pericardium line via control points of the line. From the points detected an interpolation was applied based on the cubic interpolation, which was also improved to avoid incorrect interpolation that occurs when the two variables are non-monotonic. The method is validated by using a set of 40 CT images of the heart of 40 human subjects. In 62.5% of the cases only minimal user intervention was required and the results compared favourably with the results obtained by the manual process.     

Image-based midsole insert design and the material effects on heel plantar pressure distribution during simulated walking loads

The primary objective of this paper is to study the use of medical image-based finite element (FE) modelling in subject-specific midsole design and optimisation for heel pressure reduction using a midsole plug under the calcaneus area (UCA). Plugs with different relative dimensions to the size of the calcaneus of the subject have been incorporated in the heel region of the midsole. The FE foot model was validated by comparing the numerically predicted plantar pressure with biomechanical tests conducted on the same subject. For each UCA midsole plug design, the effect of material properties and plug thicknesses on the plantar pressure distribution and peak pressure level during the heel strike phase of normal walking was systematically studied. The results showed that the UCA midsole insert could effectively modify the pressure distribution, and its effect is directly associated with the ratio of the plug dimension to the size of the calcaneus bone of the subject. A medium hardness plug with a size of 95% of the calcaneus has achieved the best performance for relieving the peak pressure in comparison with the pressure level for a solid midsole without a plug, whereas a smaller plug with a size of 65% of the calcaneus insert with a very soft material showed minimum beneficial effect for the pressure relief.     

Hybrid Nature Inspired SMO-GBM Classifier for Exudate Classification on Fundus Retinal Images

Background: The diabetic retinopathy can result in loss of vision if not detected in the earlier stages. Exudates are the lesions which play a crucial role in early diagnosis of diabetic retinopathy. The localization of exudates lesions with high values of performance metrics is complicated due to presence of blood vessels and other noisy artifacts. Method: We present computer aided system for classification of retinal fundus images using a novel nature inspired spider monkey optimization for parameter tuning of gradient boosting machines classifier. The image enhancement has been performed with histogram equalization and contourlet transform. The pixels belonging to optic disc region are detected and eliminated using circular Hough transform and Otsu's segmentation method. We have employed Kirsch's matrices for blood vessel detection. The GLCM based feature vector extraction has been employed for textural features. The classification has been performed with hybrid SMO-GBM classifier. Result: We have utilized the STARE database for validation of proposed technique. The proposed system can effectively classify entire image set from test data. The SMO-GBM classifier can further sub-segregate into sub classes with an average accuracy of 97.5%. Conclusion: The proposed approach provides detection and grading of diabetic retinopathy. The abnormality is further categories as soft, moderate and severe. The hybrid SMO-GBM classifier yields a better statistical metrics than the existing exudates classification approaches.     

Mapping tropical forest fractional cover from coarse spatial resolution remote sensing imagery

At regional to global scales the only feasible approach to mapping and monitoring forests is through the use of coarse spatial resolution remotely sensed imagery. Significant errors in mapping may arise as such imagery may be dominated by pixels of mixed land cover composition which cannot be accommodated by conventional mapping approaches. This may lead to incorrect assessments of forest extent and thereby processes such as deforestation which may propagate into studies of environmental change. A method to unmix the class composition of image pixels is presented and used to map tropical forest cover in part of the Mato Grosso, Brazil. This method is based on an artificial neural network and has advantages over other techniques used in remote sensing. Fraction images depicting the proportional class coverage in each pixel were produced and shown to correspond closely to the actual land cover. The predicted and actual forest cover were, for instance, strongly correlated (up to r = 0.85, significant at the 99% level of confidence) and the predicted extent of forest over the test site much closer to the actual extent than that derived from a conventional approach to mapping from remotely sensed imagery.     

Visual Aid Reviews

Books Practice in Human Biology — Questions for AS and A-level Jane Vellacott and Sarah Side, 2001. Hodder &; Stoughton, 202pp. £9.99. ISBN 0 340 78958 1 Reviewed by Pauline Lowrie

Books Microbiology and Biotechnology Pauline Lowrie and Susan Wells, 2000. Cambridge University Press, 108pp. £7.95. ISBN 0 52178723 8 Reviewed by Julia Milligan

Books Microbiology: an introduction Gerard J Tortora, Berdell R Funke, and Christine L Case, 2000. Pearson Education, 902pp. £33.00. ISBN 0 8053 7554 6 Reviewed by Steve Hall

Books Encyclopedia of Genetics Eric C R Reeve (Ed.), 2001. Fitzroy Dearborn, 952pp. £105.00. ISBN 1 884964 34 6 Reviewed by Michael Reiss

Books Coral Fish Linda Pitkin, 2001. The Natural History Museum, 112pp. £9.95. ISBN 0 565 09152 2 Reviewed by Julia Glanville

Books Liasons of Life Tom Wakeford, 2001. John Wiley &; Sons Ltd, 212pp. £17.95. ISBN 0 471 39972 8 Reviewed by Roger Delpech     

Recent developments in molecular simulation approaches to study spherical virus capsids

Viruses are particularly challenging systems to study via molecular simulation methods. Virus capsids typically consist of over 100 subunit proteins and reach dimensions of over 100 nm; solvated viruses capsid systems can be over 1 million atoms in size. In this review, I will present recent developments which have attempted to overcome the significant computational expense to perform simulations which can inform experimental studies, make useful predictions about biological phenomena and calculate material properties relevant to nanotechnology design efforts.     

基于多尺度分割和决策树算法的山区遥感影像变化检测方法——以四川攀西地区为例

山区遥感影像变化检测面临地形效应明显、空间异质性高等不利因素的影响,构建适用于复杂地形条件的变化检测方法一直是遥感应用研究的难点。在对影像进行多尺度分割的基础上,构建对象的多种光谱、形状及地形特征,将地形阴影、物候等造成的虚假变化当作"未变化"训练样本,利用决策树算法自动提取检测规则,建立复杂地形条件下面向对象的遥感影像变化检测方法,并将该方法用于四川攀西山区1989年和2009年TM影像的检测试验,最后对方法的不足和改进措施进行了讨论。主要结论包括:(1)文中构建的方法能够有效减弱山区复杂地形条件对遥感影像变化检测的不利影响,采用地面调查数据和分层随机采样的总体验证精度为93.57%,Kappa系数为0.8706。(2)C5.0决策树算法对于只有"变化"和"未变化"两种类别且同类间训练样本高度异质化的影像分类仍能取得较好的效果,具有较好的鲁棒性和适应能力。通过将地形、物候等引起的虚假变化当作"未变化"训练样本可以有效提高检测精度。(3)光谱特征仍是TM影像遥感分析的主要信息源,影像间NDVI的差值对于植被覆盖区域土地覆盖格局变化的检测具有良好的表征作用。(4)攀西地区1989—2009年间土地覆盖格局变化明显且与人类活动关系密切,典型的驱动方式包括退耕还林(草)工程、水利工程建设和矿山开采等。共检测出变化面积740.2km2,占影像总面积的2.49%。     

Improving 2‐DE gel image denoising using contourlets

One of the most commonly used methods for protein separation is 2‐DE. After 2‐DE gel scanning, images with a plethora of spot features emerge that are usually contaminated by inherent noise. The objective of the denoising process is to remove noise to the extent that the true spots are recovered correctly and accurately i.e. without introducing distortions leading to the detection of false‐spot features. In this paper we propose and justify the use of the contourlet transform as a tool for 2‐DE gel images denoising. We compare its effectiveness with state‐of‐the‐art methods such as wavelets‐based multiresolution image analysis and spatial filtering. We show that contourlets not only achieve better average S/N performance than wavelets and spatial filters, but also preserve better spot boundaries and faint spots and alter less the intensities of informative spot features, leading to more accurate spot volume estimation and more reliable spot detection, operations that are essential to differential expression proteomics for biomarkers discovery.