Death Wish: What Washington Court Should Do When a Capital Defendant Wants to Die

The Washington Supreme Court held in State v. Dodd that a capital defendant may waive general review of conviction and sentence, and failed to determine whether a defendant may also withhold all mitigating evidence from the sentencing proceeding. The holding limits appellate oversight of death sentences to a degree that fails to ensure Washington\u27s interest in reliable capital punishment. The court should have required general review of both conviction and sentencing in all capital cases. It also should have established a procedure for third-party presentation of mitigating evidence on behalf of capital defendants who insist on withholding such evidence

An important role for Myb-MuvB and its target gene KIF23 in a mouse model of lung adenocarcinoma

The conserved Myb-MuvB (MMB) multiprotein complex has an important role in transcriptional activation of mitotic genes. MMB target genes are overexpressed in several different cancer types and their elevated expression is associated with an advanced tumor state and a poor prognosis. This suggests that MMB could contribute to tumorigenesis by mediating overexpression of mitotic genes. However, although MMB has been extensively characterized biochemically, the requirement for MMB in tumorigenesis in vivo has not been investigated. Here we demonstrate that MMB is required for tumor formation in a mouse model of lung cancer driven by oncogenic K-RAS. We also identify a requirement for the mitotic kinesin KIF23, a key target gene of MMB, in tumorigenesis. RNA interference-mediated depletion of KIF23 inhibited lung tumor formation in vivo and induced apoptosis in lung cancer cell lines. Our results suggest that inhibition of KIF23 could be a strategy for treatment of lung cancer

Identifying functional modules in protein–protein interaction networks: an integrated exact approach

Motivation: With the exponential growth of expression and protein–protein interaction (PPI) data, the frontier of research in systems biology shifts more and more to the integrated analysis of these large datasets. Of particular interest is the identification of functional modules in PPI networks, sharing common cellular function beyond the scope of classical pathways, by means of detecting differentially expressed regions in PPI networks. This requires on the one hand an adequate scoring of the nodes in the network to be identified and on the other hand the availability of an effective algorithm to find the maximally scoring network regions. Various heuristic approaches have been proposed in the literature

Community Outreach through Genomics Education Partnership

The J Craig Venter Institute (JCVI) has recently partnered with undergraduate university faculty to expand the scope of education and outreach program as part of the NIAID’s BRC initiative, by joining forces with faculty members participating in the Genomics Education Partnership (GEP). The goal of the GEP is to provide opportunities for undergraduate students to participate in genomics research and gain hands on experience. Faculty members trained on annotation methodologies and tools during the Prokaryotic Annotation Workshop conducted at JCVI, impart their knowledge in the classroom as part of the semester course. As a pilot project, we are currently collaborating with 3 groups lead by a faculty member, spread across 3 universities in the community curation of bacterial genomes. Each participating undergraduate group collectively annotates a specific bacterial genome that was sequenced at JCVI and run through the automatic annotation pipeline. Remote access to genome sequence data, pre-computed gene predictions, search results, automatic annotation and bioinformatics analysis is provided through our web-based manual annotation tool, MANATEE. The students log into JCVI genome databases with user specific ids and password and learn to annotate single genes, entire metabolic pathways leading to analysis of a question that may be unique to the genome being analyzed. Users of the genome data receive dedicated support and guidance from our in house annotation experts on the usage of JCVI’s tools and annotation methodologies. Through this exercise, the undergraduate students are introduced to concepts of genomics and bioinformatics and gain deeper understanding of the concepts of cellular metabolism and disease pathology, which may lead them to making scientific research their career path. Some groups are focusing on genome specific pathways and plan to conduct wet lab experiments to understand unique genome features. We are highly encouraged that this model of web based, remote access, community annotation has been successful and propose to leverage the community of annotators to update annotations of pathogen genomes in Pathema-BRC

Applications of Quantum Algorithms to Partially Observable Markov Decision Processes

Abstract Due to the enormous processing gains that are theoretically achievable by using quantum algorithms instead of classical algorithms to solve rather generic classes of numerical problems, it makes sense that one should evaluate their potential applicability, appropriateness, and efficiency for solving virtually any computationally intensive task. Since many types of control and optimization problems may be couched in terms of partially observable Markov decision processes (POMDPs), and since solutions to these types of problems are invariably extremely difficult to obtain, the use of quantum algorithms to help solve POMDP problems is investigated here. Quantum algorithms are indeed found likely to provide significant efficiency improvements in several computationally intensive tasks associated with solving POMDPs, particularly in the areas of searching, optimization, and parameter optimization and estimation

Germinal Center B Cell-Like (GCB) and Activated B Cell-Like (ABC) Type of Diffuse Large B Cell Lymphoma (DLBCL): Analysis of Molecular Predictors, Signatures, Cell Cycle State and Patient Survival

Aiming to find key genes and events, we analyze a large data set on diffuse large B-cell lymphoma (DLBCL) gene-expression (248 patients, 12196 spots). Applying the loess normalization method on these raw data yields improved survival predictions, in particular for the clinical important group of patients with medium survival time. Furthermore, we identify a simplified prognosis predictor, which stratifies different risk groups similarly well as complex signatures