Design, Modeling, And Control Of Three-port Converters For Solar Power Applications

This paper describes the results of research into multi-port converter design and control, specifically a pair of three-port topologies based on the half-bridge and full-bridge topologies. These converters are capable of simultaneous and independent regulation of two out of their three ports, while the third port provides the power balance in the system. A dynamic model was developed for each topology to aid in testing and for designing the control loops. The models were then used to design the control structures, and the results were tested in Simulink. In addition, a basic outline of a system level architecture to control multiple converters working in parallel is presented. To improve the reliability of this system, output current sharing controls were also developed. Finally, one of the topologies is analyzed in detail in order to obtain a set of design equations that can be used to improve the efficiency, weight, and cost of the converter for a specific application

Analysis of Archived Residual Newborn Screening Blood Spots After Whole Genome Amplification

Deidentified newborn screening bloodspot samples (NBS) represent a valuable potential resource for genomic research if impediments to whole exome sequencing of NBS deoxyribonucleic acid (DNA), including the small amount of genomic DNA in NBS material, can be overcome. For instance, genomic analysis of NBS could be used to define allele frequencies of disease-associated variants in local populations, or to conduct prospective or retrospective studies relating genomic variation to disease emergence in pediatric populations over time. In this study, we compared the recovery of variant calls from exome sequences of amplified NBS genomic DNA to variant calls from exome sequencing of non-amplified NBS DNA from the same individuals. Results: Using a standard alignment-based Genome Analysis Toolkit (GATK), we find 62,000-76,000 additional variants in amplified samples. After application of a unique kmer enumeration and variant detection method (RUFUS), only 38,000-47,000 additional variants are observed in amplified gDNA. This result suggests that roughly half of the amplification-introduced variants identified using GATK may be the result of mapping errors and read misalignment. Conclusions: Our results show that it is possible to obtain informative, high-quality data from exome analysis of whole genome amplified NBS with the important caveat that different data generation and analysis methods can affect variant detection accuracy, and the concordance of variant calls in whole-genome amplified and non-amplified exomes.National Institute of Health P01HD067244, NS076465, R01ES021006Nutritional Science

Downregulated Expression of Ly-6-ThB on Developing T Cells Marks CD4+CD8+ Subset Undergoing Selection in the Thymus

Interaction of TCRs on CD4+CD8+ immature T cell with MHC-peptide complexes on stromal cells is required for positive and negative selection in the thymus. Identification and characterization of a subpopulation of CD4+CD8+ thymocytes undergoing selection in the thymus will aid in understanding the mechanisms underlying lineage commitment and thymic selection. Herein, we describe the expression of Ly-6 ThB on developing thymocytes. The majority of CD4+CD8+ thymocytes express Ly-6 ThB at high levels. Its expression is downregulated in a subset of CD4+CD8+ thymocytes as well as in mature CD4+CD8- and CD4-CD8+ T cells. More importantly, interaction of TCR/coreceptor with the self-MHC-peptide contributes to the downregulation of ThB expression on developing thymocytes. These findings indicate that downregulation of ThB on CD4+CD8+ thymocytes identifies a unique subset (CD4+CD8+ThBneg–low) of thymocytes that has received the initial signals for thymic selection but have not yet downregulated the CD4 and CD8 cell surface expression. In addition, these results also indicate that a high frequency (Ÿ20–40%) of CD4+CD8+ immature thymocytes receive these initial signals during thymic selection

The Hymenoptera Genome Database

The Hymenoptera Genome Database (HGD) is an informatics resource supporting genomics of hymenopteran insect species. This relational database implements open-source software and components providing access to curated data contributed by an extensive, active research community. HGD includes the genome sequences and annotation data of honey bee _Apis mellifera_ and its pathogens ("http://BeeBase.org":BeeBase.org) the parasitoid wasp _Nasonia vitripennis_ ("http://NasoniaBase.org":NasoniaBase.org) and a portal to the genomes of six species of ants. Together, these species cover approximately 200 MY in the phylogeny of Hymenoptera, allowing to leverage genetic, genome sequence, and gene expression data, as well as the biological knowledge of related model organisms. The availability of resources across an order greatly facilitates comparative genomics and enhances our understanding of the biology of agriculturally important Hymenoptera species through genomics. HGD has supported research contributions from an extensive community from almost 80 institutions in 14 countries. Community annotation efforts are made possible thanks to a remote connection to a Chado database by Apollo Genome Annotation client software. Curated data at HGD includes predicted and annotated gene sets supported with evidence tracks such as ESTs/cDNAs, small RNA sequences and GC composition domains. Data at HGD can be queried using genome browsers and / or BLAST/PSI-BLAST servers, and it may also be downloaded to perform local searches. We encourage the public to access and contribute data to HGD at "http://HymenopteraGenome.org":HymenopteraGenome.org.

This poster contains material included in an article accepted for publication in Nucl. Acids Res.©: 2011. The Database Issue. Published by Oxford University Press

Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding

We introduce a set of algorithms (Het-node2vec) that extend the original node2vec node-neighborhood sampling method to heterogeneous multigraphs, i.e. networks characterized by multiple types of nodes and edges. The resulting random walk samples capture both the structural characteristics of the graph and the semantics of the different types of nodes and edges. The proposed algorithms can focus their attention on specific node or edge types, allowing accurate representations also for underrepresented types of nodes/edges that are of interest for the prediction problem under investigation. These rich and well-focused representations can boost unsupervised and supervised learning on heterogeneous graphs.Comment: 20 pages, 5 figure

MGMT enrichment and second gene co-expression in hematopoietic progenitor cells using separate or dual-gene lentiviral vectors

AbstractThe DNA repair gene O6-methylguanine-DNA methyltransferase (MGMT) allows efficient in vivo enrichment of transduced hematopoietic stem cells (HSC). Thus, linking this selection strategy to therapeutic gene expression offers the potential to reconstitute diseased hematopoietic tissue with gene-corrected cells. However, different dual-gene expression vector strategies are limited by poor expression of one or both transgenes. To evaluate different co-expression strategies in the context of MGMT-mediated HSC enrichment, we compared selection and expression efficacies in cells cotransduced with separate single-gene MGMT and GFP lentivectors to those obtained with dual-gene vectors employing either encephalomyocarditis virus (EMCV) internal ribosome entry site (IRES) or foot and mouth disease virus (FMDV) 2A elements for co-expression strategies. Each strategy was evaluated in vitro and in vivo using equivalent multiplicities of infection (MOI) to transduce 5-fluorouracil (5-FU) or Lin−Sca-1+c-kit+ (LSK)-enriched murine bone marrow cells (BMCs). The highest dual-gene expression (MGMT+GFP+) percentages were obtained with the FMDV-2A dual-gene vector, but half of the resulting gene products existed as fusion proteins. Following selection, dual-gene expression percentages in single-gene vector cotransduced and dual-gene vector transduced populations were similar. Equivalent MGMT expression levels were obtained with each strategy, but GFP expression levels derived from the IRES dual-gene vector were significantly lower. In mice, vector-insertion averages were similar among cells enriched after dual-gene vectors and those cotransduced with single-gene vectors. These data demonstrate the limitations and advantages of each strategy in the context of MGMT-mediated selection, and may provide insights into vector design with respect to a particular therapeutic gene or hematologic defect

Creating a honey bee consensus gene set

BACKGROUND: We wished to produce a single reference gene set for honey bee (Apis mellifera). Our motivation was twofold. First, we wished to obtain an improved set of gene models with increased coverage of known genes, while maintaining gene model quality. Second, we wished to provide a single official gene list that the research community could further utilize for consistent and comparable analyses and functional annotation. RESULTS: We created a consensus gene set for honey bee (Apis mellifera) using GLEAN, a new algorithm that uses latent class analysis to automatically combine disparate gene prediction evidence in the absence of known genes. The consensus gene models had increased representation of honey bee genes without sacrificing quality compared with any one of the input gene predictions. When compared with manually annotated gold standards, the consensus set of gene models was similar or superior in quality to each of the input sets. CONCLUSION: Most eukaryotic genome projects produce multiple gene sets because of the variety of gene prediction programs. Each of the gene prediction programs has strengths and weaknesses, and so the multiplicity of gene sets offers users a more comprehensive collection of genes to use than is available from a single program. On the other hand, the availability of multiple gene sets is also a cause for uncertainty among users as regards which set they should use. GLEAN proved to be an effective method to combine gene lists into a single reference set

Mouse models of preterm birth: Suggested assessment and reporting guidelines

Preterm birth affects approximately 1 out of every 10 births in the United States, leading to high rates of mortality and long-term negative health consequences. To investigate the mechanisms leading to preterm birth so as to develop prevention strategies, researchers have developed numerous mouse models of preterm birth. However, the lack of standard definitions for preterm birth in mice limits our field\u27s ability to compare models and make inferences about preterm birth in humans. In this review, we discuss numerous mouse preterm birth models, propose guidelines for experiments and reporting, and suggest markers that can be used to assess whether pups are premature or mature. We argue that adoption of these recommendations will enhance the utility of mice as models for preterm birth

The promises of large language models for protein design and modeling.

The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the language of proteins invite the application and adaptation of LLMs to protein modelling and design. Considering the impressive results of GPT-4 and other recently developed LLMs in processing, generating and translating human languages, we anticipate analogous results with the language of proteins. Indeed, protein language models have been already trained to accurately predict protein properties, generate novel functionally characterized proteins, achieving state-of-the-art results. In this paper we discuss the promises and the open challenges raised by this novel and exciting research area, and we propose our perspective on how LLMs will affect protein modeling and design