Rhodium complexes bearing tetradentate diamine-bis(phenolate) ligands

Using tetradentate, dianionic ligands, several new rhodium complexes have been prepared. Some of these diamine-bis(phenolate) compounds, are active for C–H activation of benzene. These complexes are air and thermally stable. All four complexes were characterized by X-ray diffraction

Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies

BackgroundAutomated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies.ResultsUsing a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs.ConclusionThe study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes.<br /

Gentrepid V2.0: a web server for candidate disease gene prediction

Contains fulltext : 124935.pdf (publisher's version ) (Open Access)BACKGROUND: Candidate disease gene prediction is a rapidly developing area of bioinformatics research with the potential to deliver great benefits to human health. As experimental studies detecting associations between genetic intervals and disease proliferate, better bioinformatic techniques that can expand and exploit the data are required. DESCRIPTION: Gentrepid is a web resource which predicts and prioritizes candidate disease genes for both Mendelian and complex diseases. The system can take input from linkage analysis of single genetic intervals or multiple marker loci from genome-wide association studies. The underlying database of the Gentrepid tool sources data from numerous gene and protein resources, taking advantage of the wealth of biological information available. Using known disease gene information from OMIM, the system predicts and prioritizes disease gene candidates that participate in the same protein pathways or share similar protein domains. Alternatively, using an ab initio approach, the system can detect enrichment of these protein annotations without prior knowledge of the phenotype. CONCLUSIONS: The system aims to integrate the wealth of protein information currently available with known and novel phenotype/genotype information to acquire knowledge of biological mechanisms underpinning disease. We have updated the system to facilitate analysis of GWAS data and the study of complex diseases. Application of the system to GWAS data on hypertension using the ICBP data is provided as an example. An interesting prediction is a ZIP transporter additional to the one found by the ICBP analysis. The webserver URL is https://www.gentrepid.org/

Deficiency of Phosphoinositide 3-Kinase Enhancer Protects Mice From Diet-Induced Obesity and Insulin Resistance

OBJECTIVE: Phosphoinositide 3-kinase enhancer A (PIKE-A) is a proto-oncogene that promotes tumor growth and transformation by enhancing Akt activity. However, the physiological functions of PIKE-A in peripheral tissues are unknown. Here, we describe the effect of PIKE deletion in mice and explore the role of PIKE-A in obesity development. RESEARCH DESIGN AND METHODS: Whole-body PIKE knockout mice were generated and subjected to high-fat–diet feeding for 20 weeks. The glucose tolerance, tissue-specific insulin sensitivity, adipocyte differentiation, and lipid oxidation status were determined. The molecular mechanism of PIKE in the insulin signaling pathway was also studied. RESULTS: We show that PIKE-A regulates obesity development by modulating AMP-activated protein kinase (AMPK) phosphorylation. PIKE-A is important for insulin to suppress AMPK phosphorylation. The expression of PIKE-A is markedly increased in adipose tissue of obese mice, whereas depletion of PIKE-A inhibits adipocyte differentiation. PIKE knockout mice exhibit a prominent phenotype of lipoatrophy and are resistant to high-fat diet–induced obesity, liver steatosis, and diabetes. PIKE knockout mice also have augmented lipid oxidation, which is accompanied by enhanced AMPK phosphorylation in both muscle and adipose tissue. Moreover, insulin sensitivity is improved in PIKE-A–deficient muscle and fat, thus protecting the animals from diet-induced diabetes. CONCLUSIONS: Our results suggest that PIKE-A is implicated in obesity and associated diabetes development by negatively regulating AMPK activity

Analysis of genome-wide association study data using the protein knowledge base

Article 9

Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer

Purpose: In some proton therapy facilities, patient alignment relies on two 2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed imaging is available. The visibility of the tumor in kV images is limited since the patient's 3D anatomy is projected onto a 2D plane, especially when the tumor is behind high-density structures such as bones. This can lead to large patient setup errors. A solution is to reconstruct the 3D CT image from the kV images obtained at the treatment isocenter in the treatment position. Methods: An asymmetric autoencoder-like network built with vision-transformer blocks was developed. The data was collected from 1 head and neck patient: 2 orthogonal kV images (1024x1024 voxels), 1 3D CT with padding (512x512x512) acquired from the in-room CT-on-rails before kVs were taken and 2 digitally-reconstructed-radiograph (DRR) images (512x512) based on the CT. We resampled kV images every 8 voxels and DRR and CT every 4 voxels, thus formed a dataset consisting of 262,144 samples, in which the images have a dimension of 128 for each direction. In training, both kV and DRR images were utilized, and the encoder was encouraged to learn the jointed feature map from both kV and DRR images. In testing, only independent kV images were used. The full-size synthetic CT (sCT) was achieved by concatenating the sCTs generated by the model according to their spatial information. The image quality of the synthetic CT (sCT) was evaluated using mean absolute error (MAE) and per-voxel-absolute-CT-number-difference volume histogram (CDVH). Results: The model achieved a speed of 2.1s and a MAE of <40HU. The CDVH showed that <5% of the voxels had a per-voxel-absolute-CT-number-difference larger than 185 HU. Conclusion: A patient-specific vision-transformer-based network was developed and shown to be accurate and efficient to reconstruct 3D CT images from kV images.Comment: 9 figure

Reproducibility of a Long-Range Swept-Source Optical Coherence Tomography Ocular Biometry System and Comparison with Clinical Biometers

Objective To demonstrate a novel swept source optical coherence tomography (SS-OCT) imaging device using a vertical cavity surface-emitting laser (VCSEL) capable of imaging the full eye length and to introduce a method using this device for noncontact ocular biometry. To compare the measurements of intraocular distances using this SS-OCT instrument with commercially available optical and ultrasound biometers. To evaluate the intersession reproducibility of measurements of intraocular distances using SS-OCT. Design Evaluation of technology. Participants Twenty eyes of 10 healthy subjects imaged at the New England Eye Center at Tufts Medical Center and Massachusetts Institute of Technology between May and September 2012. Methods Averaged central depth profiles were extracted from volumetric SS-OCT datasets. The intraocular distances, such as central corneal thickness (CCT), aqueous depth (AD), anterior chamber depth (ACD), crystalline lens thickness (LT), vitreous depth (VD), and axial length (AL), were measured and compared with a partial coherence interferometry device (IOLMaster; Carl Zeiss Meditec, Inc., Dublin, CA) and an immersion ultrasound (IUS) A-scan biometer (Axis-II PR; Quantel Medical, Inc., Cournon d'Auvergne Cedex, France). Main Outcome Measures Reproducibility of the measurements of intraocular distances, correlation coefficients, and intraclass correlation coefficients. Results The standard deviations of the repeated measurements of intraocular distances using SS-OCT were 6 μm (CCT), 16 μm (ACD), 14 μm (AD), 13 μm (LT), 14 μm (VD), and 16 μm (AL). Strong correlations among all 3 biometric instruments were found for AL (r > 0.98). The AL measurement using SS-OCT correlates better with the IOLMaster (r=0.998) than with IUS (r=0.984). The SS-OCT and IOLMaster measured higher AL values than ultrasound (175 and 139 μm, respectively). No statistically significant difference in ACD between the optical (SS-OCT or IOLMaster) and ultrasound methods was detected. High intersession reproducibility of SS-OCT measurements of all intraocular distances was observed with intraclass correlation coefficients >0.99. Conclusions The SS-OCT using VCSEL technology enables full eye length imaging and high-precision, noncontact ocular biometry. The measurements with the prototype SS-OCT instrument correlate well with commercial biometers. The SS-OCT biometry has the potential to provide clinically useful comprehensive biometric parameters for pre- and postoperative eye evaluation.National Institutes of Health (U.S.) (Grant R01-EY011289-27)National Institutes of Health (U.S.) (Grant R01-EY013178-12)National Institutes of Health (U.S.) (Grant R01-EY013516-09)National Institutes of Health (U.S.) (Grant R01-EY019029-04)National Institutes of Health (U.S.) (Grant R44EY022864-01)National Institutes of Health (U.S.) (Grant R01-CA075289-16)National Institutes of Health (U.S.) (Grant R01-NS057476-05)National Institutes of Health (U.S.) (Grant R44CA101067-05)United States. Air Force Office of Scientific Research (Grant FA9550-10-1-0551)United States. Air Force Office of Scientific Research (Grant FA9550-10-1-0063)Thorlabs, Inc