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

Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic.

Spatial scan statistics are widely used in various fields. The performance of these statistics is influenced by parameters, such as maximum spatial cluster size, and can be improved by parameter selection using performance measures. Current performance measures are based on the presence of clusters and are thus inapplicable to data sets without known clusters. In this work, we propose a novel overall performance measure called maximum clustering set-proportion (MCS-P), which is based on the likelihood of the union of detected clusters and the applied dataset. MCS-P was compared with existing performance measures in a simulation study to select the maximum spatial cluster size. Results of other performance measures, such as sensitivity and misclassification, suggest that the spatial scan statistic achieves accurate results in most scenarios with the maximum spatial cluster sizes selected using MCS-P. Given that previously known clusters are not required in the proposed strategy, selection of the optimal maximum cluster size with MCS-P can improve the performance of the scan statistic in applications without identified clusters

Online marketing of a chosen company

The main goal of this thesis was evaluate the impact of indiviudal tools of communication mix on the results of the online campaign of the chosen brand. This campaign lasted for almost three months. The core of the campaign was consumer competition. The main goal of this campaign was sale increase and brand awareness. In the first part, I dealt with the tools of the communication mix from the point of view of the use in marketing and measuring their performance. In the second part, I analyzed the way brand worked with individual tools of commuciation mix and then I evaluated the tools according to established criteria such as impresion, interaction, web traffic and conversions. I used analytical tools to evaluate the success of each of the communication mix tools. After evaluating the communication mix tools, I suggested recommendations for further brand communication on the Internet. The biggest impact on the results of the campaign had Facebook, which clearly dominated among all of the tools from the point of view of all the criteria

Incidence of measles in Henan in May 2009 and clusters detected with default (a) and selected maximum spatial cluster size using MCS-P (b).

<p>Administrative codes of 42 clustering counties are labeled. RR of each counties are presented as bar.</p

Average MCS-P and other measures in 6000-three-8.

<p>Average MCS-P and other measures in 6000-three-8.</p

Agreements of MCS-P with the other performance measures in different scenarios.

<p>Agreements of MCS-P with the other performance measures in different scenarios.</p

Average performance measures for different maximum spatial cluster sizes in 600-two-1.

<p>Average performance measures for different maximum spatial cluster sizes in 600-two-1.</p

Average performance measures for different maximum spatial cluster sizes in 6000-three-8.

<p>Average performance measures for different maximum spatial cluster sizes in 6000-three-8.</p

Average MCS-P and other measures in 6000-two-16.

<p>This figure shows two stages of the relationship between average MCS-P and the other performance measures. The vertical line shows the cut-off point where the first value close to the optimal results of MCS-P is achieved.</p

Different counties in clusters detected using maximum spatial cluster sizes of 2% and 50%.

<p>Different counties in clusters detected using maximum spatial cluster sizes of 2% and 50%.</p