Motivation: Genetic variation that disrupts gene function by altering gene splicing between individuals can substantially influence traits and disease. In those cases, accurately predicting the effects of genetic variation on splicing can be highly valuable for investigating the mechanisms underlying those traits and diseases. While methods have been developed to generate high quality computational predictions of gene structures in reference genomes, the same methods perform poorly when used to predict the potentially deleterious effects of genetic changes that alter gene splicing between individuals. Underlying that discrepancy in predictive ability are the common assumptions by reference gene finding algorithms that genes are conserved, well-formed and produce functional proteins. Results: We describe a probabilistic approach for predicting recent changes to gene structure that may or may not conserve function. The model is applicable to both coding and non-coding genes, and can be trained on existing gene annotations without requiring curated examples of aberrant splicing. We apply this model to the problem of predicting altered splicing patterns in the genomes of individual humans, and we demonstrate that performing gene-structure prediction without relying on conserved coding features is feasible. The model predicts an unexpected abundance of variants that create de novo splice sites, an observation supported by both simulations and empirical data from RNA-seq experiments. While these de novo splice variants are commonly misinterpreted by other tools as coding or non-coding variants of little or no effect, we find that in some cases they can have large effects on splicing activity and protein products and we propose that they may commonly act as cryptic factors in disease. Availability and implementation: The software is available from geneprediction.org/SGRF. Supplementary information: Supplementary information is available at Bioinformatics online.

MOTIVATION: The accurate interpretation of genetic variants is critical for characterizing genotype-phenotype associations. Because the effects of genetic variants can depend strongly on their local genomic context, accurate genome annotations are essential. Furthermore, as some variants have the potential to disrupt or alter gene structure, variant interpretation efforts stand to gain from the use of individualized annotations that account for differences in gene structure between individuals or strains. RESULTS: We describe a suite of software tools for identifying possible functional changes in gene structure that may result from sequence variants. ACE ('Assessing Changes to Exons') converts phased genotype calls to a collection of explicit haplotype sequences, maps transcript annotations onto them, detects gene-structure changes and their possible repercussions, and identifies several classes of possible loss of function. Novel transcripts predicted by ACE are commonly supported by spliced RNA-seq reads, and can be used to improve read alignment and transcript quantification when an individual-specific genome sequence is available. Using publicly available RNA-seq data, we show that ACE predictions confirm earlier results regarding the quantitative effects of nonsense-mediated decay, and we show that predicted loss-of-function events are highly concordant with patterns of intolerance to mutations across the human population. ACE can be readily applied to diverse species including animals and plants, making it a broadly useful tool for use in eukaryotic population-based resequencing projects, particularly for assessing the joint impact of all variants at a locus. AVAILABILITY AND IMPLEMENTATION: ACE is written in open-source C ++ and Perl and is available from geneprediction.org/ACE. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

This special issue serves as a forum of high visibility and synergy to promote the momentum of spectral CT. Through a rigorous peer-review process, 14 high-quality papers1 have been included from leading groups around the world. These papers give a panorama of the state of the art, addressing challenges in detector and source technologies, image reconstruction, material decomposition, performance evaluation, biomedical, and other applications.

This paper describes a scholarship and student mentoring program created to increase female enrollment and graduates in our Schools of Engineering and Science. This program was developed and administered jointly by both of the schools, and the admissions, financial aid, student life, career development, and learning resource departments. Scholarship recipients were required to participate in a mentoring program in which they met regularly with an upper-division peer mentor. Student participants also engaged in academic preparation activities to help develop study, organization, and test-taking skills for academic success. Further, social activities, such as field trips and dinners, were included to build camaraderie. A team of faculty was highly involved as academic advisors to all students in the program and as an advisory board for the grant overall. The program has been effective in increasing female enrollments and improving retention in both schools, particularly in engineering.

Summary form only given. One of the driving forces behind the semiconductor industry has been Moore's law. In abstract Moore's law says the number of transistors per square inch on integrated circuits will double every 18 months. Doubling transistor count in rough approximation doubles complexity. To keep the same cost, yield at the new process node must be similar to the previous node. Further, to maintain the cost of test as a percentage of AUC, then test time per transistor or cost of test per second have to be reduced by 50% in the same time lines. The research and development driven in yield management, tester development, test development and DFT have allowed an ongoing cost profile that allows for economic profile required to sustain semiconductor expansion. This talk will explore both historical and real time issues related to complexity and the test industry.

In the era of complex SOC semiconductors, the test matrices and complexity of system, manufacturing and pre-silicon design test analysis are continuously evolving, as are the demands of the customers for improved quality at reduced cost. Business constraints that drive test solutions can make or break products, if not companies, in today's market. Business constraints, namely cost and time to market, require that we strengthen and enlarge the current model for inter-company cooperation and development. This calls for improving existing systems modeling capabilities to achieve the reliability and predictability these systems models have the potential to offer. Significant advancements have been made; however, for full optimization of test cost and product quality, more is needed

Silicon yield once was dominated by contaminants and particulates, making yield a process issue. But with today's electronics supply chain, multiple suspects may be indicted on manufacturability issues. Who is responsible for preventative actions in manufacturability and yield? The AUTHORs, representing a foundry, a fabless company, an IP provider, two EDA vendors, and an IC design team, will discuss the problems and the solutions for achieving manufacturability and yield goals.