Use HypE to Hide Association Rules by Adding Items

During business collaboration, partners may benefit through sharing data. People may use data mining tools to discover useful relationships from shared data. However, some relationships are sensitive to the data owners and they hope to conceal them before sharing. In this paper, we address this problem in forms of association rule hiding. A hiding method based on evolutionary multi-objective optimization (EMO) is proposed, which performs the hiding task by selectively inserting items into the database to decrease the confidence of sensitive rules below specified thresholds. The side effects generated during the hiding process are taken as optimization goals to be minimized. HypE, a recently proposed EMO algorithm, is utilized to identify promising transactions for modification to minimize side effects. Results on real datasets demonstrate that the proposed method can effectively perform sanitization with fewer damages to the non-sensitive knowledge in most cases.     

The Induction of Phagocytic Activation by Mixtures of the Water Chlorination By‐Products,Dichloroacetate‐ and Trichloroacetate,in Mice after Subchronic Exposure

In this study, groups of B6C3F1 male mice were treated with dichloroacetate (DCA), trichloroacetate (TCA), and mixtures of the compounds (Mix I, II, and III) daily by gavage, for 13 weeks. The tested doses were 7.5, 15, and 30 mg DCA/kg/day and 12.5, 25, and 50 mg TCA/kg/day. The DCA: TCA ratios in Mix I, II, and III were 7.5:12.5, 15:25, and 30:50 mg/kg/day, respectively. Peritoneal lavage cells were collected at the end of the treatment period and assayed for the biomarkers of phagocytic activation, including superoxide anion and tumor necrosis factor‐alpha production, and myeloperoxidase activity. The mixtures produced nonlinear effects on the biomarkers of phagocytic activation, with Mix I and II effects were found to be additive, but Mix III effects were found to be less than additive. © 2013 Wiley Periodicals, Inc. J BiochemMol Toxicol 27:237‐242, 2013; View this article online at wileyonlinelibrary.com . DOI 10.1002/jbt.21476     

Prediction and Analysis of Antibody Amyloidogenesis from Sequences

Antibody amyloidogenesis is the aggregation of soluble proteins into amyloid fibrils that is one of major causes of the failures of humanized antibodies. The prediction and prevention of antibody amyloidogenesis are helpful for restoring and enhancing therapeutic effects. Due to a large number of possible germlines, the existing method is not practical to predict sequences of novel germlines, which establishes individual models for each known germline. This study proposes a first automatic and across-germline prediction method (named AbAmyloid) capable of predicting antibody amyloidogenesis from sequences. Since the amyloidogenesis is determined by a whole sequence of an antibody rather than germline-dependent properties such as mutated residues, this study assess three types of germline-independent sequence features (amino acid composition, dipeptide composition and physicochemical properties). AbAmyloid using a Random Forests classifier with dipeptide composition performs well on a data set of 12 germlines. The within- and across-germline prediction accuracies are 83.10% and 83.33% using Jackknife tests, respectively, and the novel-germline prediction accuracy using a leave-one-germline-out test is 72.22%. A thorough analysis of sequence features is conducted to identify informative properties for further providing insights to antibody amyloidogenesis. Some identified informative physicochemical properties are amphiphilicity, hydrophobicity, reverse turn, helical structure, isoelectric point, net charge, mutability, coil, turn, linker, nuclear protein, etc. Additionally, the numbers of ubiquitylation sites in amyloidogenic and non-amyloidogenic antibodies are found to be significantly different. It reveals that antibodies less likely to be ubiquitylated tend to be amyloidogenic. The method AbAmyloid capable of automatically predicting antibody amyloidogenesis of novel germlines is implemented as a publicly available web server at http://iclab.life.nctu.edu.tw/abamyloid.     

AFD-Net: Apple Foliar Disease multi classification using deep learning on plant pathology dataset

Plant and Soil - Plant diseases significantly affect the crop, so their identification is very important. Correct identification of these diseases is crucial for establishing a good disease control...     

A quantum dot-aptamer beacon using a DNA intercalating dye as the FRET reporter: application to label-free thrombin detection

A new quantum dot (QD)-aptamer (apt) beacon that acts by folding-induced dissociation of a DNA intercalating dye, BOBO-3(B), is demonstrated with label-free thrombin detection. The beacon, denoted as QD-apt:B, is constructed by (1) coupling of a single-stranded thrombin aptamer to Qdot 565 via EDC/Sulfo-NHS chemistry and (2) staining the duplex regions of the aptamer on QD with excess BOBO-3 before thrombin binding. When mixing a thrombin sample with QD-apt:B, BOBO-3 is competed away from the beacon due to target-induced aptamer folding, which then causes a decrease in QD fluorescence resonance energy transfer (FRET)-mediated BOBO-3 emission and achieves thrombin quantitation. In this work, the effects of Mg(2+), coupling time, and aptamer type on the beacon's performances are investigated and discussed thoroughly with various methods, including transmission electron microscopy (TEM), dynamic light scattering (DLS), and two-color differential gel electrophoresis. Using the best aptamer beacon (HTQ37), we attain highly specific and wide-range detection (from nM to μM) of thrombin in buffer, and the beacon can sense nM-range thrombin in 15% diluted serum. Compared to the reported QD aptamer assays, our method is advantageous from the aspect of using a simple sensory unit design without losing the detection sensitivity. Therefore, we consider the QD-apt:B beacon a potential alternative to immuno-reagents and an effective tool to study nucleic acid folding on QD as well.     

Mice with a D190N mutation in the gene encoding rhodopsin: a model for human autosomal-dominant retinitis pigmentosa

Rhodopsin is the G protein-coupled receptor in charge of initiating signal transduction in rod photoreceptor cells upon the arrival of the photon. D190N (Rho(D190n)), a missense mutation in rhodopsin, causes autosomal-dominant retinitis pigmentosa (adRP) in humans. Affected patients present hyperfluorescent retinal rings and progressive rod photoreceptor degeneration. Studies in humans cannot reveal the molecular processes causing the earliest stages of the condition, thus necessitating the creation of an appropriate animal model. A knock-in mouse model with the D190N mutation was engineered to study the pathogenesis of the disease. Electrophysiological and histological findings in the mouse were similar to those observed in human patients, and the hyperfluorescence pattern was analogous to that seen in humans, confirming that the D190N mouse is an accurate model for the study of adRP.