Sains Permakanan: Makanan Berfungsi

Kebanyakan pakar farmakologi mengakui bahawa permakanan penting unluk kesihatan dan kesejahteraan. Pada tahun 500 sebelum masihi, hubungan antara farmakologi dengan pemakanan adalah rapat sebagaimana yang dinyatakan oleh Hippocrates, 'Biarkan makanan menjadi ubat anda dan biarkan ubat meniadi makanan anda. Hanya rawatan semulajadi yang memenuhi kritera tersebut.' Orang Yunan kuno membahagikan perubatan kepada tiga kategori, atau diet, farmasi dan perubatan surgeri. Hal ini menunjukkan bahawa adalah makanan sebahagian daripada perubatan yang diamalkan. Dalam sistem perubatan tradisional seperti Ayurveda dan perubatan tradisional Cina, juga tidak ada perbezaan antara makanan dengan ubalan ialtu rnakanan adalah bahagan yang penting untuk mencegah penyakit dan menjaga kesihatan

Learning to Segment Microscopy Images with Lazy Labels

The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for segmenting bioimages that can contain numerous object instances with thin separations. In this paper, we introduce a deep convolutional neural network for microscopy image segmentation. Annotation issues are circumvented by letting the network being trainable on coarse labels combined with only a very small number of images with pixel-wise annotations. We call this new labelling strategy `lazy' labels. Image segmentation is stratified into three connected tasks: rough inner region detection, object separation and pixel-wise segmentation. These tasks are learned in an end-to-end multi-task learning framework. The method is demonstrated on two microscopy datasets, where we show that the model gives accurate segmentation results even if exact boundary labels are missing for a majority of annotated data. It brings more flexibility and efficiency for training deep neural networks that are data hungry and is applicable to biomedical images with poor contrast at the object boundaries or with diverse textures and repeated patterns

Extensive population genetic structure in the giraffe

<p>Abstract</p> <p>Background</p> <p>A central question in the evolutionary diversification of large, widespread, mobile mammals is how substantial differentiation can arise, particularly in the absence of topographic or habitat barriers to dispersal. All extant giraffes (<it>Giraffa camelopardalis</it>) are currently considered to represent a single species classified into multiple subspecies. However, geographic variation in traits such as pelage pattern is clearly evident across the range in sub-Saharan Africa and abrupt transition zones between different pelage types are typically not associated with extrinsic barriers to gene flow, suggesting reproductive isolation.</p> <p>Results</p> <p>By analyzing mitochondrial DNA sequences and nuclear microsatellite loci, we show that there are at least six genealogically distinct lineages of giraffe in Africa, with little evidence of interbreeding between them. Some of these lineages appear to be maintained in the absence of contemporary barriers to gene flow, possibly by differences in reproductive timing or pelage-based assortative mating, suggesting that populations usually recognized as subspecies have a long history of reproductive isolation. Further, five of the six putative lineages also contain genetically discrete populations, yielding at least 11 genetically distinct populations.</p> <p>Conclusion</p> <p>Such extreme genetic subdivision within a large vertebrate with high dispersal capabilities is unprecedented and exceeds that of any other large African mammal. Our results have significant implications for giraffe conservation, and imply separate <it>in situ </it>and <it>ex situ </it>management, not only of pelage morphs, but also of local populations.</p

The mammals of Angola

Scientific investigations on the mammals of Angola started over 150 years ago, but information remains scarce and scattered, with only one recent published account. Here we provide a synthesis of the mammals of Angola based on a thorough survey of primary and grey literature, as well as recent unpublished records. We present a short history of mammal research, and provide brief information on each species known to occur in the country. Particular attention is given to endemic and near endemic species. We also provide a zoogeographic outline and information on the conservation of Angolan mammals. We found confirmed records for 291 native species, most of which from the orders Rodentia (85), Chiroptera (73), Carnivora (39), and Cetartiodactyla (33). There is a large number of endemic and near endemic species, most of which are rodents or bats. The large diversity of species is favoured by the wide range of habitats with contrasting environmental conditions, while endemism tends to be associated with unique physiographic settings such as the Angolan Escarpment. The mammal fauna of Angola includes 2 Critically Endangered, 2 Endangered, 11 Vulnerable, and 14 Near-Threatened species at the global scale. There are also 12 data deficient species, most of which are endemics or near endemics to the countryinfo:eu-repo/semantics/publishedVersio

Multi-organ detection in 3D fetal ultrasound with machine learning

3D ultrasound (US) is a promising technique to perform automatic extraction of standard planes for fetal anatomy assessment. This requires prior organ localization, which is difficult to obtain with direct learning approaches because of the high variability in fetus size and orientation in US volumes. In this paper, we propose a methodology to overcome this spatial variability issue by scaling and automatically aligning volumes in a common 3D reference coordinate system. This preprocessing allows the organ detection algorithm to learn features that only encodes the anatomical variability while discarding the fetus pose. All steps of the approach are evaluated on 126 manually annotated volumes, with an overall mean localization error of 11.9 mm, showing the feasibility of multi-organ detection in 3D fetal US with machine learning

Multi-organ detection in 3D fetal ultrasound with machine learning

3D ultrasound (US) is a promising technique to perform automatic extraction of standard planes for fetal anatomy assessment. This requires prior organ localization, which is difficult to obtain with direct learning approaches because of the high variability in fetus size and orientation in US volumes. In this paper, we propose a methodology to overcome this spatial variability issue by scaling and automatically aligning volumes in a common 3D reference coordinate system. This preprocessing allows the organ detection algorithm to learn features that only encodes the anatomical variability while discarding the fetus pose. All steps of the approach are evaluated on 126 manually annotated volumes, with an overall mean localization error of 11.9 mm, showing the feasibility of multi-organ detection in 3D fetal US with machine learning

Automatic myocardium segmentation in late-enhancement MRI

\u3cp\u3eWe propose a novel automatic method to segment the myocardium on late-enhancement cardiac MR (LE CMR) images with a multi-step approach. First, in each slice of the LE CMR volume, a geometrical template is deformed so that its borders fit the myocardial contours. The second step consists in introducing a shape prior of the left ventricle. To do so, we use the cine MR sequence that is acquired along with the LE CMR volume. As the myocardial contours can be more easily automatically obtained on this data, they are used to build a 3D mesh representing the left ventricle geometry and the underlying myocardium thickness. This mesh is registered towards the contours obtained with the geometrical template, then locally adjusted to guarantee that scars are included inside the final segmentation. The quantitative evaluation on 27 volumes (272 slices) shows robust and accurate results.\u3c/p\u3

Comprehensive segmentation of cine cardiac MR images

\u3cp\u3eA typical Cardiac Magnetic Resonance (CMR) examination includes acquisition of a sequence of short-axis (SA) and long-axis (LA) images covering the cardiac cycle. Quantitative analysis of the heart function requires segmentation of the left ventricle (LV) SA images, while segmented LA views allow more accurate estimation of the basal slice and can be used for slice registration. Since manual segmentation of CMR images is very tedious and time-consuming, its automation is highly required. In this paper, we propose a fully automatic 2D method for segmenting LV consecutively in LA and SA images. The approach was validated on 35 patients giving mean segmentation error smaller than one pixel, both for LA and SA, and accurate LV volume measurements.\u3c/p\u3