Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Thermoplastic starch

Starch is a biopolymer that is widely available from agriculture/nature and thus is renewable and cheap. In addition, starch-based materials are biodegradable, offering a great advantage over traditional nonbiodegradable synthetic polymers. However, the poor processibility and product performance of starch have greatly impeded the wide application of starch in real applications. This paper reviews the current developments of the production of thermoplastic starch as a commodity renewable material. The biological origins of the feedstock, formulation development, processing requirements, as well as the important aspects that need to be addressed when designing a product from starch are discussed. It is hoped that this paper will provide insights into thermoplastic starch-based materials trends and and inspiration for future research

Development and Implementation of a Corriedale Ovine Brain Atlas for Use in Atlas-Based Segmentation

<div><p>Segmentation is the process of partitioning an image into subdivisions and can be applied to medical images to isolate anatomical or pathological areas for further analysis. This process can be done manually or automated by the use of image processing computer packages. Atlas-based segmentation automates this process by the use of a pre-labelled template and a registration algorithm. We developed an ovine brain atlas that can be used as a model for neurological conditions such as Parkinson’s disease and focal epilepsy. 17 female Corriedale ovine brains were imaged in-vivo in a 1.5T (low-resolution) MRI scanner. 13 of the low-resolution images were combined using a template construction algorithm to form a low-resolution template. The template was labelled to form an atlas and tested by comparing manual with atlas-based segmentations against the remaining four low-resolution images. The comparisons were in the form of similarity metrics used in previous segmentation research. Dice Similarity Coefficients were utilised to determine the degree of overlap between eight independent, manual and atlas-based segmentations, with values ranging from 0 (no overlap) to 1 (complete overlap). For 7 of these 8 segmented areas, we achieved a Dice Similarity Coefficient of 0.5–0.8. The amygdala was difficult to segment due to its variable location and similar intensity to surrounding tissues resulting in Dice Coefficients of 0.0–0.2. We developed a low resolution ovine brain atlas with eight clinically relevant areas labelled. This brain atlas performed comparably to prior human atlases described in the literature and to intra-observer error providing an atlas that can be used to guide further research using ovine brains as a model and is hosted online for public access.</p></div

Segmentation Metrics.

<p>Segmentation Metrics.</p

The (a) skulled template and (b) deskulling mask.

<p>Variability displayed in extracortical areas reinforces the reason why deskulling is an important step in pre-processing.</p

Deskulling Metrics.

<p>Deskulling Metrics.</p