Dietary Restriction Induced Longevity is Mediated by Nuclear Receptor NHR-62 in Caenorhabditis elegans

Dietary restriction (DR) extends lifespan in a wide variety of species, yet the underlying mechanisms are not well understood. Here we show that the C. elegans HNF4a- related nuclear hormone receptor NHR-62 is required for metabolic and physiologic responses associated with DR-induced longevity. nhr-62 mediates the longevity of eat- 2 mutants, a genetic mimetic of dietary restriction, and blunts the longevity response of DR induced by bacterial food dilution at low nutrient levels. Metabolic changes associated with DR, including decreased Oil Red O staining, increased autophagy, and changes in fatty acid composition are partly reversed by mutation of nhr-62. Expression profiles reveal that several hundred genes induced by DR depend on the activity of NHR-62, including a putative lipase required for the DR response. This study provides critical evidence that nuclear hormone receptors regulate the DR response, suggesting hormonal and metabolic control of life span.

Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data

The advent of next generation sequencing technologies (NGS) has expanded the area of genomic research, offering high coverage and increased sensitivity over older microarray platforms. Although the current cost of next generation sequencing is still exceeding that of microarray approaches, the rapid advances in NGS will likely make it the platform of choice for future research in differential gene expression. Connectivity mapping is a procedure for examining the connections among diseases, genes and drugs by differential gene expression initially based on microarray technology, with which a large collection of compound-induced reference gene expression profiles have been accumulated. In this work, we aim to test the feasibility of incorporating NGS RNA-Seq data into the current connectivity mapping framework by utilizing the microarray based reference profiles and the construction of a differentially expressed gene signature from a NGS dataset. This would allow for the establishment of connections between the NGS gene signature and those microarray reference profiles, alleviating the associated incurring cost of re-creating drug profiles with NGS technology. We examined the connectivity mapping approach on a publicly available NGS dataset with androgen stimulation of LNCaP cells in order to extract candidate compounds that could inhibit the proliferative phenotype of LNCaP cells and to elucidate their potential in a laboratory setting. In addition, we also analyzed an independent microarray dataset of similar experimental settings. We found a high level of concordance between the top compounds identified using the gene signatures from the two datasets. The nicotine derivative cotinine was returned as the top candidate among the overlapping compounds with potential to suppress this proliferative phenotype. Subsequent lab experiments validated this connectivity mapping hit, showing that cotinine inhibits cell proliferation in an androgen dependent manner. Thus the results in this study suggest a promising prospect of integrating NGS data with connectivity mapping. © 2013 McArt et al.

Invariant Information Local Sub-map Filter (IILSF) for Efficient Simultaneous Localisation and Mapping of Large Environments

This paper presents an Invariant Information Local Sub-map Filter (IILSF) as a technique for consistent Simultaneous Localisation and Mapping (SLAM) in a large environment. It harnesses the benefits of sub-map technique to improve the consistency and efficiency of Extended Kalman Filter (EKF) based SLAM. The IILSF makes use of invariant information obtained from estimated locations of features in independent sub-maps, instead of incorporating every observation directly into the global map. Then the global map is updated at regular intervals. Applying this technique to the EKF based SLAM algorithm: (a) reduces the computational complexity of maintaining the global map estimates and (b) simplifies transformation complexities and data association ambiguities usually experienced in fusing sub-maps together. Simulation results show that the method was able to accurately fuse local map observations to generate an efficient and consistent global map, in addition to significantly reducing computational cost and data association ambiguities.

The Role of Growth Trajectories in Classifying Fetal Growth Restriction

OBJECTIVE: To examine the validity of a growth trajectory method to discriminate between pathologically and constitutionally undergrown fetuses using repeated measures of estimated fetal weight.

METHODS: In a prospective, observational, multicenter study in Ireland, 1,116 women with a growth-restricted fetus diagnosed participated with the objective of evaluating ultrasound findings as predictors of pediatric morbidity and mortality. Fetal growth trajectories were based on estimated fetal weight.

RESULTS: Between 22 weeks of gestation and term, two fetal growth trajectories were identified: normal (96.7%) and pathologic (3.3%). Compared with the normal trajectory, the pathologic trajectory was associated with an increased risk for preeclampsia (odds ratio [OR] 8.1, 95% confidence interval [CI] 2.6–23.4), increased umbilical artery resistance at 30 weeks of gestation (OR 12.6, 95% CI 4.6–34.1) or 34 weeks of gestation (OR 28.0, 95% CI 8.9–87.7), reduced middle cerebral artery resistance at 30 weeks of gestation (OR 0.33, 95% CI 0.12–0.96) or 34 weeks of gestation (OR 0.14, 95% CI 0.03–0.74), lower gestational age at delivery (mean 32.02 weeks of gestation compared with 38.02 weeks of gestation; P<.001), and higher perinatal complications (OR 21.5, 95% CI 10.5–44.2). In addition, 89.2% of newborns with pathologic fetal growth were admitted to neonatal intensive care units compared with 25.9% of those with normal growth.

CONCLUSIONS: Fetal growth trajectory analysis reliably differentiated fetuses with a pathologic growth pattern among a group of women with growth-restricted fetuses. With further development, this approach could provide clarity to how we define, identify, and ultimately manage pathologic fetal growth.

LEVEL OF EVIDENCE: II

Mapping worlds? Excavating cartographic encounters in Plantation Ireland through GIS

This paper uses the analytical potential of Geographical Information Systems (GIS) to explore processes of map production and circulation in early-seventeenth century Ireland. The paper focuses on a group of historic maps, attributed to Josias Bodley, which were commissioned in 1609 by the English Crown to assist in the Plantation of Ulster. Through GIS and digitizing map-features, and in particular by quantifying map-distortion, it is possible to examine how these maps were made, and by whom. Statistical analyses of spatial data derived from the GIS are shown to provide a methodological basis for ‘excavating’ historical geographies of Plantation map-making. These techniques, when combined with contemporary written sources, reveal further insight on the ‘cartographic encounters’ taking place between surveyors and map-makers working in Ireland in the early 1600s, opening up the ‘mapping worlds’ which linked Ireland and Britain through the networks and embodied practices of Bodley and his map-makers.

cudaMap: a GPU accelerated program for gene expression connectivity mapping

Background: Modern cancer research often involves large datasets and the use of sophisticated statistical techniques. Together these add a heavy computational load to the analysis, which is often coupled with issues surrounding data accessibility. Connectivity mapping is an advanced bioinformatic and computational technique dedicated to therapeutics discovery and drug re-purposing around differential gene expression analysis. On a normal desktop PC, it is common for the connectivity mapping task with a single gene signature to take >2h to complete using sscMap, a popular Java application that runs on standard CPUs (Central Processing Units). Here, we describe new software, cudaMap, which has been implemented using CUDA C/C++ to harness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing times for connectivity mapping.

Results: cudaMap can identify candidate therapeutics from the same signature in just over thirty seconds when using an NVIDIA Tesla C2050 GPU. Results from the analysis of multiple gene signatures, which would previously have taken several days, can now be obtained in as little as 10 minutes, greatly facilitating candidate therapeutics discovery with high throughput. We are able to demonstrate dramatic speed differentials between GPU assisted performance and CPU executions as the computational load increases for high accuracy evaluation of statistical significance.

Conclusion: Emerging 'omics' technologies are constantly increasing the volume of data and information to be processed in all areas of biomedical research. Embracing the multicore functionality of GPUs represents a major avenue of local accelerated computing. cudaMap will make a strong contribution in the discovery of candidate therapeutics by enabling speedy execution of heavy duty connectivity mapping tasks, which are increasingly required in modern cancer research. cudaMap is open source and can be freely downloaded from http://purl.oclc.org/NET/cudaMap.

Nanoscale mapping of oxygen vacancy kinetics in nanocrystalline Samarium doped ceria thin films

The position-dependent oxygen vacancy dynamics induced by a biased scanning probe microscopy tip in Samarium doped ceria thin films grown on MgO (100) substrates is investigated. The granularity of the samples gives rise to spatially dependent local electrochemical activity, as explored by electrochemical strain microscopy. The kinetics of the oxygen vacancy relaxation process is investigated separately for grain boundaries and grains. Higher oxygen vacancy concentration variation and slower diffusion are observed in the grain boundary regions as compared to the grains.

Using geostatistical Bayesian Updating to integrate airborne radiometrics and soil geochemistry to improve mapping for mineral exploration

Mineral exploration programmes around the world use data from remote sensing, geophysics and direct sampling. On a regional scale, the combination of airborne geophysics and ground-based geochemical sampling can aid geological mapping and economic minerals exploration. The fact that airborne geophysical and traditional soil-sampling data are generated at different spatial resolutions means that they are not immediately comparable due to their different sampling density. Several geostatistical techniques, including indicator cokriging and collocated cokriging, can be used to integrate different types of data into a geostatistical model. With increasing numbers of variables the inference of the cross-covariance model required for cokriging can be demanding in terms of effort and computational time. In this paper a Gaussian-based Bayesian updating approach is applied to integrate airborne radiometric data and ground-sampled geochemical soil data to maximise information generated from the soil survey, to enable more accurate geological interpretation for the exploration and development of natural resources. The Bayesian updating technique decomposes the collocated estimate into a production of two models: prior and likelihood models. The prior model is built from primary information and the likelihood model is built from secondary information. The prior model is then updated with the likelihood model to build the final model. The approach allows multiple secondary variables to be simultaneously integrated into the mapping of the primary variable. The Bayesian updating approach is demonstrated using a case study from Northern Ireland where the history of mineral prospecting for precious and base metals dates from the 18th century. Vein-hosted, strata-bound and volcanogenic occurrences of mineralisation are found. The geostatistical technique was used to improve the resolution of soil geochemistry, collected one sample per 2 km2, by integrating more closely measured airborne geophysical data from the GSNI Tellus Survey, measured over a footprint of 65 x 200 m. The directly measured geochemistry data were considered as primary data in the Bayesian approach and the airborne radiometric data were used as secondary data. The approach produced more detailed updated maps and in particular maximized information on mapped estimates of zinc, copper and lead. Greater delineation of an elongated northwest/southeast trending zone in the updated maps strengthened the potential to investigate stratabound base metal deposits.

Lymphatic mapping and sentinel node biopsy in breast cancer

Background Sentinel lymph node biopsy is a recently developed, minimally invasive technique for staging the axilla in patients with breast cancer. It has been suggested that this technique will avoid the morbidity associated with more extensive axillary dissection. A wide range of different methods and materials has been employed for lymphatic mapping, but there has been little consensus on the most reliable and reproducible technique.

Methods This is a comprehensive review of all published literature on sentinel node biopsy in breast cancer, using the Medline and Embase databases and cross-referencing of major articles on the subject.

Results and conclusion Sentinel node biopsy is a valid technique in breast cancer management, providing valuable axillary staging information. The optimal technique of lymphatic mapping utilizes a combination of vital blue dye and radiolabelled colloid. However, there remain controversial issues which require to be resolved before sentinel node biopsy becomes a widely accepted part of breast cancer care.