Optimizing MRI sequences and images for MRI-based stereotactic radiosurgery treatment planning

AimDevelopment of MRI sequences and processing methods for the production of images appropriate for direct use in stereotactic radiosurgery (SRS) treatment planning.BackgroundMRI is useful in SRS treatment planning, especially for patients with brain lesions or anatomical targets that are poorly distinguished by CT, but its use requires further refinement. This methodology seeks to optimize MRI sequences to generate distortion-free and clinically relevant MR images for MRI-only SRS treatment planning.Materials and methodsWe used commercially available SRS MRI-guided radiotherapy phantoms and eight patients to optimize sequences for patient imaging. Workflow involved the choice of correct MRI sequence(s), optimization of the sequence parameters, evaluation of image quality (artifact free and clinically relevant), measurement of geometrical distortion, and evaluation of the accuracy of our offline correction algorithm.ResultsCT images showed a maximum deviation of 1.3 mm and minimum deviation of 0.4 mm from true fiducial position for SRS coordinate definition. Interestingly, uncorrected MR images showed maximum deviation of 1.2 mm and minimum of 0.4 mm, comparable to CT images used for SRS coordinate definition. After geometrical correction, we observed a maximum deviation of 1.1 mm and minimum deviation of only 0.3 mm.ConclusionOur optimized MRI pulse sequences and image correction technique show promising results; MR images produced under these conditions are appropriate for direct use in SRS treatment planning.     

Polyunsaturated Fatty Acid Desaturation Is a Mechanism for Glycolytic NAD+ Recycling

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Leioproctus rosellae sp. n., the first record of the genus from northern South America (Hymenoptera, Colletidae)

Leioproctus Smith is a diverse colletine genus found in the Australian region and primarily temperate areas of South America. A new species of Leioproctus subgenus Perditomorpha Ashmead, Leioproctus rosellae Gonzalez, sp. n., from a tropical dry forest of the Caribbean coast of Colombia is described and figured. This is the first record of the genus from northern South America.     

Sampling bees in tropical forests and agroecosystems: a review

Bees are the predominant pollinating taxa, providing a critical ecosystem service upon which many angiosperms rely for successful reproduction. Available data suggests that bee populations worldwide are declining, but scarce data in tropical regions precludes assessing their status and distribution, impact on ecological services, and response to management actions. Herein, we reviewed >150 papers that used six common sampling methods (pan traps, baits, Malaise traps, sweep nets, timed observations and aspirators) to better understand their strengths and weaknesses, and help guide method selection to meet research objectives and development of multi-species monitoring approaches. Several studies evaluated the effectiveness of sweep nets, pan traps, and malaise traps, but only one evaluated timed observations, and none evaluated aspirators. Only five studies compared two or more of the remaining four sampling methods to each other. There was little consensus regarding which method would be most reliable for sampling multiple species. However, we recommend that if the objective of the study is to estimate abundance or species richness, malaise traps, pan traps and sweep nets are the most effective sampling protocols in open tropical systems; conversely, malaise traps, nets and baits may be the most effective in forests. Declining bee populations emphasize the critical need in method standardization and reporting precision. Moreover, we recommend reporting a catchability coefficient, a measure of the interaction between the resource (bee) abundance and catching effort. Melittologists could also consider existing methods, such as occupancy models, to quantify changes in distribution and abundance after modeling heterogeneity in trapping probability, and consider the possibility of developing monitoring frameworks that draw from multiple sources of data.     

Identification of Novel Type 2 Diabetes Candidate Genes Involved in the Crosstalk between the Mitochondrial and the Insulin Signaling Systems

Type 2 Diabetes (T2D) is a highly prevalent chronic metabolic disease with strong co-morbidity with obesity and cardiovascular diseases. There is growing evidence supporting the notion that a crosstalk between mitochondria and the insulin signaling cascade could be involved in the etiology of T2D and insulin resistance. In this study we investigated the molecular basis of this crosstalk by using systems biology approaches. We combined, filtered, and interrogated different types of functional interaction data, such as direct protein–protein interactions, co-expression analyses, and metabolic and signaling dependencies. As a result, we constructed the mitochondria-insulin (MITIN) network, which highlights 286 genes as candidate functional linkers between these two systems. The results of internal gene expression analysis of three independent experimental models of mitochondria and insulin signaling perturbations further support the connecting roles of these genes. In addition, we further assessed whether these genes are involved in the etiology of T2D using the genome-wide association study meta-analysis from the DIAGRAM consortium, involving 8,130 T2D cases and 38,987 controls. We found modest enrichment of genes associated with T2D amongst our linker genes (p = 0.0549), including three already validated T2D SNPs and 15 additional SNPs, which, when combined, were collectively associated to increased fasting glucose levels according to MAGIC genome wide meta-analysis (p = 8.12×10⁻⁵). This study highlights the potential of combining systems biology, experimental, and genome-wide association data mining for identifying novel genes and related variants that increase vulnerability to complex diseases.     

MicroRNAs Regulate Pituitary Development,and MicroRNA 26b Specifically Targets Lymphoid Enhancer Factor 1 (Lef-1), Which Modulates Pituitary Transcription Factor 1 (Pit-1) Expression

To understand the role of microRNAs (miRNAs) in pituitary development, a group of pituitary-specific miRNAs were identified, and Dicer1 was then conditionally knocked out using the Pitx2-Cre mouse, resulting in the loss of mature miRNAs in the anterior pituitary. The Pitx2-Cre/Dicer1 mutant mice demonstrate growth retardation, and the pituitaries are hypoplastic with an abnormal branching of the anterior lobe, revealing a role for microRNAs in pituitary development. Growth hormone, prolactin, and thyroid-stimulating hormone β-subunit expression were decreased in the Dicer1 mutant mouse, whereas proopiomelanocortin and luteinizing hormone β-subunit expression were normal in the mutant pituitary. Further analyses revealed decreased Pit-1 and increased Lef-1 expression in the mutant mouse pituitary, consistent with the repression of the Pit-1 promoter by Lef-1. Lef-1 directly targets and represses the Pit-1 promoter. miRNA-26b (miR-26b) was identified as targeting Lef-1 expression, and miR-26b represses Lef-1 in pituitary and non-pituitary cell lines. Furthermore, miR-26b up-regulates Pit-1 and growth hormone expression by attenuating Lef-1 expression in GH3 cells. This study demonstrates that microRNAs are critical for anterior pituitary development and that miR-26b regulates Pit-1 expression by inhibiting Lef-1 expression and may promote Pit-1 lineage differentiation during pituitary development.     

Ordered stratification to reduce heterogeneity in linkage to diabetes-related quantitative traits

Phenotypic heterogeneity complicates detection of genomic loci predisposing to type 2 diabetes, potentially obscuring or unmasking specific loci. We conducted ordered-subsets linkage analyses (OSAs) for diabetes-related quantitative traits (fasting insulin and glucose, hemoglobin A1c (HbA1c), and 28-year-time-averaged fasting plasma glucose (FPG)) from 330 families of the Framingham Offspring Study. We calculated mean BMI, waist circumference (WC), and a diabetes "age-of-onset score" for each family. We constructed subsets by adding one family at a time in increasing (lean family to obese) or decreasing (obese to lean) adiposity order, or increasing or decreasing propensity to develop diabetes at a younger age, with the OSA LOD reported as the maximum LOD observed in any subset. Permutation P values tested the hypothesis that phenotypic ordering showed stronger linkage than random ordering. On chromosome 1, ordering by increasing family mean WC increased linkage to time-averaged FPG at 256 cM from LOD = 2.4 to 3.5 (permuted P = 0.02) and to HbA1c at 180 cM from LOD = 2.0 to 3.3 (P = 0.01). On chromosome 19, ordering by decreasing WC increased linkage to fasting insulin at 68 cM from LOD = 2.7 to 4.6 (P = 0.002), and ordering by decreasing propensity to develop diabetes at a young age increased linkage to fasting insulin at 73 cM from LOD = 2.7 to 4.0 (P = 0.046). We conclude that chromosomes 1 and 19 could harbor adiposity-interacting diabetes susceptibility genes. Such interactions might also influence trait-locus associations and may be useful to consider in diabetes genome-wide association studies.