Estimation of Higher-order Regression via. Sparse Representation Model for Single Image Super-resolution Algorithm

Super-resolution algorithms generate high-resolution (HR) imagery from single or multiple low-resolution (LR) degraded images. In this paper, an efficient single image super-resolution (SR) algorithm using higher-order regression is proposed. Image patches extracted from HR image will have self-similar example patches near its corresponding location in the LR image. A higherorder regression function is learned using these self-similar example patches via. sparse representation model. The regression function is based on local approximations and henceforth estimated from the localized image patches. Taylor series is used as local approximation of the regression function and hence the zeroth order regression co-efficient will yield the local estimate of the regression function and the higher-order regression co-efficient will provide the local estimate of the higher-order derivative of the regression function. The learned higher-order regression mapping function is applied to LR image patches to approximate its corresponding HR version. The proposed super-resolution approach is evaluated with standard test images and is compared against state-of-the-art SR algorithms. It is observed that the proposed technique preserves sharp high-frequency (HF) details and reconstructs visually appealing HR images without introducing andy artifacts

An Example-Based Super-Resolution Algorithm for Selfie Images

A selfie is typically a self-portrait captured using the front camera of a smartphone. Most state-of-the-art smartphones are equipped with a high-resolution (HR) rear camera and a low-resolution (LR) front camera. As selfies are captured by front camera with limited pixel resolution, the fine details in it are explicitly missed. This paper aims to improve the resolution of selfies by exploiting the fine details in HR images captured by rear camera using an example-based super-resolution (SR) algorithm. HR images captured by rear camera carry significant fine details and are used as an exemplar to train an optimal matrix-value regression (MVR) operator. The MVR operator serves as an image-pair priori which learns the correspondence between the LR-HR patch-pairs and is effectively used to super-resolve LR selfie images. The proposed MVR algorithm avoids vectorization of image patch-pairs and preserves image-level information during both learning and recovering process. The proposed algorithm is evaluated for its efficiency and effectiveness both qualitatively and quantitatively with other state-of-the-art SR algorithms. The results validate that the proposed algorithm is efficient as it requires less than 3 seconds to super-resolve LR selfie and is effective as it preserves sharp details without introducing any counterfeit fine details

Natural Windbreaks Sustain Bird Diversity in a Tea- Dominated Landscape

Windbreaks often form networks of forest habitats that improve connectivity and thus conserve biodiversity, but little is known of such effects in the tropics. We determined bird species richness and community composition in windbreaks composed of remnant native vegetation amongst tea plantations (natural windbreaks), and compared it with the surrounding primary forests. Fifty-one, ten-minute point counts were conducted in each habitat type over three days. Despite the limited sampling period, our bird inventories in both natural windbreaks and primary forests were nearly complete, as indicated by bootstrap true richness estimator. Bird species richness and abundance between primary forests and windbreaks were similar, however a difference in bird community composition was observed. Abundances of important functional groups such as frugivores and insectivores did not vary between habitat types but nectarivores were more abundant in windbreaks, potentially as a result of the use of windbreaks as traveling routes, foraging and nesting sites. This preliminary study suggests that natural windbreaks may be important habitats for the persistence of bird species in a production landscape. However, a better understanding of the required physical and compositional characteristics for windbreaks to sustain bird communities is needed for effective conservation management

Mycobacterium tuberculosis NAD(+)-dependent DNA ligase is selectively inhibited by glycosylamines compared with human DNA ligase I

DNA ligases are important enzymes which catalyze the joining of nicks between adjacent bases of double-stranded DNA. NAD(+)-dependent DNA ligases (LigA) are essential in bacteria and are absent in humans. They have therefore been identified as novel, validated and attractive drug targets. Using virtual screening against an in-house database of compounds and our recently determined crystal structure of the NAD(+) binding domain of the Mycobacterium tuberculosis LigA, we have identified N(1), N(n)-bis-(5-deoxy-α-d-xylofuranosylated) diamines as a novel class of inhibitors for this enzyme. Assays involving M.tuberculosis LigA, T4 ligase and human DNA ligase I show that these compounds specifically inhibit LigA from M.tuberculosis. In vitro kinetic and inhibition assays demonstrate that the compounds compete with NAD(+) for binding and inhibit enzyme activity with IC(50) values in the µM range. Docking studies rationalize the observed specificities and show that among several glycofuranosylated diamines, bis xylofuranosylated diamines with aminoalkyl and 1, 3-phenylene carbamoyl spacers mimic the binding modes of NAD(+) with the enzyme. Assays involving LigA-deficient bacterial strains show that in vivo inhibition of ligase by the compounds causes the observed antibacterial activities. They also demonstrate that the compounds exhibit in vivo specificity for LigA over ATP-dependent ligase. This class of inhibitors holds out the promise of rational development of new anti-tubercular agents

Global text mining and development of pharmacogenomic knowledge resource for precision medicine

Understanding patients' genomic variations and their effect in protecting or predisposing them to drug response phenotypes is important for providing personalized healthcare. Several studies have manually curated such genotype-phenotype relationships into organized databases from clinical trial data or published literature. However, there are no text mining tools available to extract high-accuracy information from such existing knowledge. In this work, we used a semiautomated text mining approach to retrieve a complete pharmacogenomic (PGx) resource integrating disease-drug-gene-polymorphism relationships to derive a global perspective for ease in therapeutic approaches. We used an R package, pubmed.mineR, to automatically retrieve PGx-related literature. We identified 1,753 disease types, and 666 drugs, associated with 4,132 genes and 33,942 polymorphisms collated from 180,088 publications. With further manual curation, we obtained a total of 2,304 PGx relationships. We evaluated our approach by performance (precision = 0.806) with benchmark datasets like Pharmacogenomic Knowledgebase (PharmGKB) (0.904), Online Mendelian Inheritance in Man (OMIM) (0.600), and The Comparative Toxicogenomics Database (CTD) (0.729). We validated our study by comparing our results with 362 commercially used the US- Food and drug administration (FDA)-approved drug labeling biomarkers. Of the 2,304 PGx relationships identified, 127 belonged to the FDA list of 362 approved pharmacogenomic markers, indicating that our semiautomated text mining approach may reveal significant PGx information with markers for drug response prediction. In addition, it is a scalable and state-of-art approach in curation for PGx clinical utility

Centrilobular emphysema and coronary artery calcification: mediation analysis in the SPIROMICS cohort

Abstract Background Chronic obstructive pulmonary disease (COPD) is associated with a two-to-five fold increase in the risk of coronary artery disease independent of shared risk factors. This association is hypothesized to be mediated by systemic inflammation but this link has not been established. Methods We included 300 participants enrolled in the SPIROMICS cohort, 75 each of lifetime non-smokers, smokers without airflow obstruction, mild-moderate COPD, and severe-very severe COPD. We quantified emphysema and airway disease on computed tomography, characterized visual emphysema subtypes (centrilobular and paraseptal) and airway disease, and used the Weston visual score to quantify coronary artery calcification (CAC). We used the Sobel test to determine whether markers of systemic inflammation mediated a link between spirometric and radiographic features of COPD and CAC. Results FEV1/FVC but not quantitative emphysema or airway wall thickening was associated with CAC (p = 0.036), after adjustment for demographics, diabetes mellitus, hypertension, statin use, and CT scanner type. To explain this discordance, we examined visual subtypes of emphysema and airway disease, and found that centrilobular emphysema but not paraseptal emphysema or bronchial thickening was independently associated with CAC (p = 0.019). MMP3, VCAM1, CXCL5 and CXCL9 mediated 8, 8, 7 and 16% of the association between FEV1/FVC and CAC, respectively. Similar biomarkers partially mediated the association between centrilobular emphysema and CAC. Conclusions The association between airflow obstruction and coronary calcification is driven primarily by the centrilobular subtype of emphysema, and is linked through bioactive molecules implicated in the pathogenesis of atherosclerosis. Trial Registration ClinicalTrials.gov: Identifier: NCT01969344 .https://deepblue.lib.umich.edu/bitstream/2027.42/146749/1/12931_2018_Article_946.pd