HIGH-QUALITY AIMING CLASSIFICATION TO CONSIGN BUGS

To lower time cost in manual work, text classification techniques are put on conduct automatic bug triage. Within this paper, we address the issue of information reduction for bug triage, i.e., how you can lessen the scale and improve the caliber of bug data. Software companies spend over 45 percent of cost in working with software bugs. An unavoidable step of fixing bugs is bug triage, which aims to properly assign a developer to a different bug. We combine instance selection with feature selection to concurrently reduce data scale around the bug dimension and also the word dimension. To look for the order of using instance selection and have selection, we extract characteristics from historic bug data sets and make a predictive model for any new bug data set. Our work provides a technique for leveraging techniques on information systems to create reduced and-quality bug data in software development and maintenance. We empirically investigate performance of information reduction on totally 600,000 bug reviews of two large free projects, namely Eclipse and Mozilla. The outcomes reveal that our data reduction can effectively lessen the data scale and enhance the precision of bug triage

Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks

Image orientation detection requires high-level scene understanding. Humans use object recognition and contextual scene information to correctly orient images. In literature, the problem of image orientation detection is mostly confronted by using low-level vision features, while some approaches incorporate few easily detectable semantic cues to gain minor improvements. The vast amount of semantic content in images makes orientation detection challenging, and therefore there is a large semantic gap between existing methods and human behavior. Also, existing methods in literature report highly discrepant detection rates, which is mainly due to large differences in datasets and limited variety of test images used for evaluation. In this work, for the first time, we leverage the power of deep learning and adapt pre-trained convolutional neural networks using largest training dataset to-date for the image orientation detection task. An extensive evaluation of our model on different public datasets shows that it remarkably generalizes to correctly orient a large set of unconstrained images; it also significantly outperforms the state-of-the-art and achieves accuracy very close to that of humans

Synthesis and Investigation of a Radioiodinated F3 Peptide Analog as a SPECT Tumor Imaging Radioligand

A radioiodinated derivative of the tumor-homing F3 peptide, (N-(2-{3-[125I]Iodobenzoyl}aminoethyl)maleimide-F3Cys peptide, [125I]IBMF3 was developed for investigation as a SPECT tumor imaging radioligand. For this purpose, we custom synthesized a modified F3 peptide analog (F3Cys) incorporating a C-terminal cysteine residue for site-specific attachment of a radioiodinated maleimide conjugating group. Initial proof-of-concept Fluorescence studies conducted with AlexaFluor 532 C5 maleimide-labeled F3Cys showed distinct membrane and nuclear localization of F3Cys in MDA-MB-435 cells. Additionally, F3Cys conjugated with NIR fluorochrome AlexaFluor 647 C2 maleimide demonstrated high tumor specific uptake in melanoma cancer MDA-MB-435 and lung cancer A549 xenografts in nude mice whereas a similarly labeled control peptide did not show any tumor uptake. These results were also confirmed by ex vivo tissue analysis. No-carrier-added [125I]IBMF3 was synthesized by a radioiododestannylation approach in 73% overall radiochemical yield. In vitro cell uptake studies conducted with [125I]IBMF3 displayed a 5-fold increase in its cell uptake at 4 h when compared to controls. SPECT imaging studies with [125I]IBMF3 in tumor bearing nude mice showed clear visualization of MDA-MB-435 xenografts on systemic administration. These studies demonstrate a potential utility of F3 peptide-based radioligands for tumor imaging with PET or SPECT techniques

Pioglitazone is as effective as dexamethasone in a cockroach allergen-induced murine model of asthma-4

<p><b>Copyright information:</b></p><p>Taken from "Pioglitazone is as effective as dexamethasone in a cockroach allergen-induced murine model of asthma"</p><p>Respiratory Research 2007;8(1):90-90.</p><p>Published online 4 Dec 2007</p><p>PMCID:PMC2231357.</p><p></p>ebulized methacholine was measured in anesthetized mice (8 mice/group) using a plethysmograph specifically designed for whole-body measurements on small animals. Measurements were performed 24 h following final CRA challenge. *< 0.05 compared to vehicle treatment

Pioglitazone is as effective as dexamethasone in a cockroach allergen-induced murine model of asthma-1

<p><b>Copyright information:</b></p><p>Taken from "Pioglitazone is as effective as dexamethasone in a cockroach allergen-induced murine model of asthma"</p><p>Respiratory Research 2007;8(1):90-90.</p><p>Published online 4 Dec 2007</p><p>PMCID:PMC2231357.</p><p></p>e performed 24 h following final CRA challenge. . Peribronchial inflammatory cell infiltration was visualized by H&E staining. . Eosinophil infiltration was quantified by counting 100 high-powered fields per lung. . Total serum IgE levels were measured by ELISA. *< 0.05 compared to vehicle treatment

Pioglitazone is as effective as dexamethasone in a cockroach allergen-induced murine model of asthma-0

<p><b>Copyright information:</b></p><p>Taken from "Pioglitazone is as effective as dexamethasone in a cockroach allergen-induced murine model of asthma"</p><p>Respiratory Research 2007;8(1):90-90.</p><p>Published online 4 Dec 2007</p><p>PMCID:PMC2231357.</p><p></p>ebulized methacholine was measured in anesthetized mice (8 mice/group) using a plethysmograph specifically designed for whole-body measurements on small animals. Measurements were performed 24 h following final CRA challenge. *< 0.05 compared to vehicle treatment

Guar Gum, Xanthan Gum, and HPMC Can Define Release Mechanisms and Sustain Release of Propranolol Hydrochloride

The objectives were to characterize propranolol hydrochloride-loaded matrix tablets using guar gum, xanthan gum, and hydroxypropylmethylcellulose (HPMC) as rate-retarding polymers. Tablets were prepared by wet granulation using these polymers alone and in combination, and physical properties of the granules and tablets were studied. Drug release was evaluated in simulated gastric and intestinal media. Rugged tablets with appropriate physical properties were obtained. Empirical and semi-empirical models were fit to release data to elucidate release mechanisms. Guar gum alone was unable to control drug release until a 1:3 drug/gum ratio, where the release pattern matched a Higuchi profile. Matrix tablets incorporating HPMC provided near zero-order release over 12 h and erosion was a contributing mechanism. Combinations of HPMC with guar or xanthan gum resulted in a Higuchi release profile, revealing the dominance of the high viscosity gel formed by HPMC. As the single rate-retarding polymer, xanthan gum retarded release over 24 h and the Higuchi model best fit the data. When mixed with guar gum, at 10% or 20% xanthan levels, xanthan gum was unable to control release. However, tablets containing 30% guar gum and 30% xanthan gum behaved as if xanthan gum was the sole rate-retarding gum and drug was released by Fickian diffusion. Release profiles from certain tablets match 12-h literature profiles and the 24-h profile of Inderal® LA. The results confirm that guar gum, xanthan gum, and HPMC can be used for the successful preparation of sustained release oral propranolol hydrochoride tablets