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

Reward maximization justifies the transition from sensory selection at childhood to sensory integration at adulthood.

In a multisensory task, human adults integrate information from different sensory modalities--behaviorally in an optimal Bayesian fashion--while children mostly rely on a single sensor modality for decision making. The reason behind this change of behavior over age and the process behind learning the required statistics for optimal integration are still unclear and have not been justified by the conventional Bayesian modeling. We propose an interactive multisensory learning framework without making any prior assumptions about the sensory models. In this framework, learning in every modality and in their joint space is done in parallel using a single-step reinforcement learning method. A simple statistical test on confidence intervals on the mean of reward distributions is used to select the most informative source of information among the individual modalities and the joint space. Analyses of the method and the simulation results on a multimodal localization task show that the learning system autonomously starts with sensory selection and gradually switches to sensory integration. This is because, relying more on modalities--i.e. selection--at early learning steps (childhood) is more rewarding than favoring decisions learned in the joint space since, smaller state-space in modalities results in faster learning in every individual modality. In contrast, after gaining sufficient experiences (adulthood), the quality of learning in the joint space matures while learning in modalities suffers from insufficient accuracy due to perceptual aliasing. It results in tighter confidence interval for the joint space and consequently causes a smooth shift from selection to integration. It suggests that sensory selection and integration are emergent behavior and both are outputs of a single reward maximization process; i.e. the transition is not a preprogrammed phenomenon

Learning Active Fusion of Multiple Experts’ Decisions: An Attention-Based Approach

ISSN:0899-7667ISSN:1530-888

A schematic overview of the proposed framework for multisensory learning and decision making.

<p><i>s = (o¹,o²,…,o^k)</i> is the perceptual input, is the current reading of the <i>i^th</i> sensor, and is the learning block of the <i>i^th</i> sensor. For each action and based on the previously received rewards, each learning block calculates a confidence interval () on the mean of the reward distribution corresponding to the given observation and action pair. The proposed Generalization Test (G Test), tests the generalization ability of the individual source against the joint space. In case that an individual source passes the G Test, its confidence interval will be considered in the decision making phase. In decision making phase, an appropriate action based on the given intervals will be selected which considers the exploration and exploitation trade-off.</p

Analyzing the learning speed and the behavior of different methods for Experiment 1 and 2.

<p>Analyzing the learning speed and the behavior of different methods for Experiment 1 and 2.</p

The proposed Algorithm for Multisensory Learning and Decision Making.

<p>The proposed Algorithm for Multisensory Learning and Decision Making.</p

Dominancy of subspaces over time.

<p>The average dominancy percentage of different combination of sensors in decision making (LUS). Subspaces including the unreliable source have been filtered. Furthermore, dependency on the integration of reliable sensors increases over time.</p