Next‐generation sequencing has aided characterization of genomic variation. While whole‐genome sequencing may capture all possible mutations, whole‐exome sequencing remains cost‐effective and captures most phenotype‐altering mutations. Initial strategies for exome enrichment utilized a hybridization‐based capture approach. Recently, amplicon‐based methods were designed to simplify preparation and utilize smaller DNA inputs. We evaluated two hybridization capture‐based and two amplicon‐based whole‐exome sequencing approaches, utilizing both Illumina and Ion Torrent sequencers, comparing on‐target alignment, uniformity, and variant calling. While the amplicon methods had higher on‐target rates, the hybridization capture‐based approaches demonstrated better uniformity. All methods identified many of the same single‐nucleotide variants, but each amplicon‐based method missed variants detected by the other three methods and reported additional variants discordant with all three other technologies. Many of these potential false positives or negatives appear to result from limited coverage, low variant frequency, vicinity to read starts/ends, or the need for platform‐specific variant calling algorithms. All methods demonstrated effective copy‐number variant calling when evaluated against a single‐nucleotide polymorphism array. This study illustrates some differences between whole‐exome sequencing approaches, highlights the need for selecting appropriate variant calling based on capture method, and will aid laboratories in selecting their preferred approach.

This paper presents a computer-aided screening system (DREAM) that analyzes fundus images with varying illumination and fields of view, and generates a severity grade for diabetic retinopathy (DR) using machine learning. Classifiers such as the Gaussian Mixture model (GMM), k-nearest neighbor (kNN), support vector machine (SVM), and AdaBoost are analyzed for classifying retinopathy lesions from nonlesions. GMM and kNN classifiers are found to be the best classifiers for bright and red lesion classification, respectively. A main contribution of this paper is the reduction in the number of features used for lesion classification by feature ranking using Adaboost where 30 top features are selected out of 78. A novel two-step hierarchical classification approach is proposed where the nonlesions or false positives are rejected in the first step. In the second step, the bright lesions are classified as hard exudates and cotton wool spots, and the red lesions are classified as hemorrhages and micro-aneurysms. This lesion classification problem deals with unbalanced datasets and SVM or combination classifiers derived from SVM using the Dempster-Shafer theory are found to incur more classification error than the GMM and kNN classifiers due to the data imbalance. The DR severity grading system is tested on 1200 images from the publicly available MESSIDOR dataset. The DREAM system achieves 100% sensitivity, 53.16% specificity, and 0.904 AUC, compared to the best reported 96% sensitivity, 51% specificity, and 0.875 AUC, for classifying images as with or without DR. The feature reduction further reduces the average computation time for DR severity per image from 59.54 to 3.46 s.

This paper presents a novel three-stage blood vessel segmentation algorithm using fundus photographs. In the first stage, the green plane of a fundus image is preprocessed to extract a binary image after high-pass filtering, and another binary image from the morphologically reconstructed enhanced image for the vessel regions. Next, the regions common to both the binary images are extracted as the major vessels. In the second stage, all remaining pixels in the two binary images are classified using a Gaussian mixture model (GMM) classifier using a set of eight features that are extracted based on pixel neighborhood and first and second-order gradient images. In the third postprocessing stage, the major portions of the blood vessels are combined with the classified vessel pixels. The proposed algorithm is less dependent on training data, requires less segmentation time and achieves consistent vessel segmentation accuracy on normal images as well as images with pathology when compared to existing supervised segmentation methods. The proposed algorithm achieves a vessel segmentation accuracy of 95.2%, 95.15%, and 95.3% in an average of 3.1, 6.7, and 11.7 s on three public datasets DRIVE, STARE, and CHASE_DB1, respectively.

G proteins serve many functions involving the transfer of signals from cell surface receptors to intracellular effector molecules. Considerable evidence suggests that there is an interaction between G proteins and the cytoskeleton. In this report, G protein ␣ subunits G i1 ␣, G s ␣, and G o ␣ are shown to activate the GTPase activity of tubulin, inhibit microtubule assembly, and accelerate microtubule dynamics. G i ␣ inhibited polymerization of tubulin-GTP into microtubules by 80 -90% in the absence of exogenous GTP. Addition of exogenous GTP, but not guanylylimidodiphosphate, which is resistant to hydrolysis, overcame the inhibition. Analysis of the dynamics of individual microtubules by video microscopy demonstrated that G i1 ␣ increases the catastrophe frequency, the frequency of transition from growth to shortening. Thus, G␣ may play a role in modulating microtubule dynamic instability, providing a mechanism for the modification of the cytoskeleton by extracellular signals.

This paper presents a novel unsupervised iterative blood vessel segmentation algorithm using fundus images. First, a vessel enhanced image is generated by tophat reconstruction of the negative green plane image. An initial estimate of the segmented vasculature is extracted by global thresholding the vessel enhanced image. Next, new vessel pixels are identified iteratively by adaptive thresholding of the residual image generated by masking out the existing segmented vessel estimate from the vessel enhanced image. The new vessel pixels are, then, region grown into the existing vessel, thereby resulting in an iterative enhancement of the segmented vessel structure. As the iterations progress, the number of false edge pixels identified as new vessel pixels increases compared to the number of actual vessel pixels. A key contribution of this paper is a novel stopping criterion that terminates the iterative process leading to higher vessel segmentation accuracy. This iterative algorithm is robust to the rate of new vessel pixel addition since it achieves 93.2-95.35% vessel segmentation accuracy with 0.9577-0.9638 area under ROC curve (AUC) on abnormal retinal images from the STARE dataset. The proposed algorithm is computationally efficient and consistent in vessel segmentation performance for retinal images with variations due to pathology, uneven illumination, pigmentation, and fields of view since it achieves a vessel segmentation accuracy of about 95% in an average time of 2.45, 3.95, and 8 s on images from three public datasets DRIVE, STARE, and CHASE_DB1, respectively. Additionally, the proposed algorithm has more than 90% segmentation accuracy for segmenting peripapillary blood vessels in the images from the DRIVE and CHASE_DB1 datasets.

␣ and ␤␥ subunits of G proteins are thought to transduce signals from cell surface receptors to intracellular effector molecules. G ␣ and G ␤␥ have also been implicated in cell growth and differentiation, perhaps due to their association with cytoskeletal components. In this report G ␤␥ is shown to modulate the cytoskeleton by regulation of microtubule assembly. Specificity among ␤␥ species exists, as ␤1␥2 stimulates microtubule assembly, and ␤1␥1 is without any effect. Furthermore, a mutant ␤1␥2, ␤1␥2(C68S), which does not undergo prenylation and subsequent carboxyl-terminal processing on the ␥ subunit, does not stimulate the formation of microtubules. ␤ immunoreactivity was detected exclusively in the microtubule fraction after assembly in the presence of ␤1␥2, suggesting a preferential association with microtubules rather than soluble tubulin. Crude microtubule fractions from ovine brain contain G ␤␥ , and electron microscopy reveals a specific association with microtubules. The decoration of microtubules by G ␤␥ appears to be strikingly similar to the periodic pattern observed for microtubule-associated proteins, suggesting a similar site of activation of microtubule assembly by both agents. It is suggested that reformation of the cytoskeleton represents an additional cellular process mediated by G ␤␥ .G proteins play important roles in signal transduction by transferring signals from cell surface receptors to intracellular effector molecules. Although receptor-G protein-effector complexes can reconstitute hormone-sensitive signaling systems in vitro, it is likely that the regulation of receptor-G protein signaling is substantially more complex in the cell. Many studies have implicated the participation of the cytoskeleton in neurotransmitter signaling pathways (1-7). Although G proteins are likely to be membrane-bound when coupled to receptors, recent results from several laboratories suggest their association with several subcellular compartments. In Caenorhabditis elegans embryos, G ␤ is required for proper spindle orientation and transiently associates with the region of asters (the array of microtubules emanating from the centrosomes) just before and during cell division (8). Astral localization of G o␣ and G ␤ has also been observed in mammalian cells (9). In addition, G ␤␥ , microtubules, and phosphatidyl inositol 4,5-biphosphate may participate in synaptic vesicle recycling by regulating the GTPase activity of dynamin 1 (10). These studies suggest a link between microtubules and G protein signaling.Association of the signal-transducing G proteins, G s , G i1 , and G q , with the synaptic membrane tubulin has been observed previously (2)(3)(4)(5)11). Tubulin appears to activate G proteins directly, and complexes between tubulin and G ␣ have been isolated from plasma membranes. While some interaction between tubulin and G ␤␥ has been observed previously, the role of such interaction remains unclear. Modification of microtubule cytoskeleton by G ␤␥ might provide an explanation for the association of G ␤...

It has been suggested that dimeric tubulin can participate in the signal transduction process through its association with the GTP-binding (G) proteins Gs and Gi1. Using the photoaffinity GTP analog, azidoanilido-GTP, it has been shown that the transfer of nucleotide from tubulin to G alpha s and G alpha i1 is the key step of this activation. The binding sites between tubulin and Gs or G alpha i1 appear to involve microtubule polymerization domains, since G protein alpha subunits were demonstrated to inhibit microtubule assembly [Wang, N., & Rasenick, M. M. (1991) Biochemistry 30, 10957-10965]. In order to understand tubulin-G protein interaction and the nucleotide transfer process in detail, tubulin was labeled with [alpha-32P]GTP or [35S]GTP gamma S and was incubated with recombinant G alpha i1 at increasing molar ratios. Rapid filtration through nitrocellulose was used to determine nucleotide binding in the protein complex. A substantial amount of bound nucleotide was lost from tubulin during the filtration assay. However, the addition of G alpha i1 to [alpha-32P]-GTP-tubulin protected the nucleotide binding in a dose-dependent manner, suggesting a stabilization of GTP binding in the tubulin-G alpha i1 complex. G beta gamma mitigated this effect, and this was not dependent upon the presence of G alpha, suggesting a direct interaction between beta gamma and tubulin. The retinal G protein, transducin, which displayed a much lower affinity for tubulin, did not elicit similar stabilization of GTP binding, and transducin beta gamma did not release GTP from tubulin.(ABSTRACT TRUNCATED AT 250 WORDS)

The Neuregulin receptor ErbB4 is an important modulator of GABAergic interneurons and neural network synchronization. However, little is known about the endogenous ligands that engage ErbB4, the neural processes that activate them or their direct downstream targets. Here we demonstrate in cultured neurons and in acute slices that the NMDA receptor is both effector and target of Neuregulin 2 (NRG2)/ErbB4 signaling in cortical interneurons. Interneurons co-express ErbB4 and NRG2, and pro-NRG2 accumulates on cell bodies atop subsurface cisterns. NMDA receptor activation rapidly triggers shedding of the signaling-competent NRG2 extracellular domain. In turn, NRG2 promotes ErbB4 association with GluN2B-containing NMDA receptors, followed by rapid internalization of surface receptors and potent down-regulation of NMDA but not AMPA receptor currents. These effects occur selectively in ErbB4-positive interneurons, not in ErbB4-negative pyramidal neurons. Our findings reveal an intimate reciprocal relationship between ErbB4 and NMDA receptors with possible implications for the modulation of cortical microcircuits associated with cognitive deficits in psychiatric disorders.