Detection of begomovirus associated with okra leaf curl disease

Leaf curl and yellow vein mosaic viral disease is the major constraint on okra (Abelmoschus esculentus L.) production in India. Amplified fragment sequence of DNA-β showed highest similarity of 91.7% with Bhendi yellow vein mosaic virus-Tamil Nadu (AJ308425, NC_003405) and lowest similarity of 48.5% with OKLCV (NC_004093), whereas coat protein specific amplified sequence showed highest homology with isolate of Madurai, Haryana, Ludhiana and lowest homology of 92% with Mesta yellow vein mosaic Bahraich virus (MYVMBV) (EU360303). The results obtained in the present study confirm that both the viral diseases of okra reported in southern India are caused by a begomovirus associated with DNA-β in which the plants show leaf curl symptoms and never develops yellow vein mosaic and those plants which show yellow vein mosaic, never develops leaf curl symptoms even in the same rows and field. The okra leaf curl is an emerging virus disease in India.     

Making Activity Recognition Robust against Deceptive Behavior

Healthcare services increasingly use the activity recognition technology to track the daily activities of individuals. In some cases, this is used to provide incentives. For example, some health insurance companies offer discount to customers who are physically active, based on the data collected from their activity tracking devices. Therefore, there is an increasing motivation for individuals to cheat, by making activity trackers detect activities that increase their benefits rather than the ones they actually do. In this study, we used a novel method to make activity recognition robust against deceptive behavior. We asked 14 subjects to attempt to trick our smartphone-based activity classifier by making it detect an activity other than the one they actually performed, for example by shaking the phone while seated to make the classifier detect walking. If they succeeded, we used their motion data to retrain the classifier, and asked them to try to trick it again. The experiment ended when subjects could no longer cheat. We found that some subjects were not able to trick the classifier at all, while others required five rounds of retraining. While classifiers trained on normal activity data predicted true activity with ~38% accuracy, training on the data gathered during the deceptive behavior increased their accuracy to ~84%. We conclude that learning the deceptive behavior of one individual helps to detect the deceptive behavior of others. Thus, we can make current activity recognition robust to deception by including deceptive activity data from a few individuals.     

新烟碱类杀虫剂对麦扁盾蝽若虫和成虫的毒效（英文）

在伊朗, 麦扁盾蝽Eurygaster integriceps是小麦和大麦上的主要害虫。新烟碱类杀虫剂是一类具有较强活性的杀虫剂, 对刺吸式口器害虫具有良好的效果。本研究应用触杀和熏蒸两种生测方法测定了4种新烟碱类杀虫剂（噻虫嗪、吡虫啉、呋虫胺和啶虫咪）对麦扁盾蝽3龄若虫和成虫的毒效。触杀法生测结果显示, 各杀虫剂对麦扁盾蝽没有强烈的触杀活性, 但是在高浓度下（5 000 mg/L a. i.）, 可见较高的死亡率。熏蒸法生测结果显示, 所有杀虫剂对麦扁盾蝽的若虫和成虫均有较好的毒性。结果提示应用熏蒸方法可能是治理这一重要害虫的新方法。     

Genetic variability of Cotton leaf curl betasatellite in Northern India

Cotton is an important crop and its production is affected by various disease pathogens. Monopartite begomovirus associated betasatellites cause Cotton leaf curl disease (CLCuD) in Northern India. In order to access the occurrence and genetic variability of Cotton leaf curl betasatellites, an extensive field survey was conducted in states of Rajasthan, Punjab and Haryana. We selected the betasatellite sequence for analysis as they are reported as important for disease severity and sequence variability. Based on the field observations, the disease incidence ranged from 30% to 80% during the survey. Full genome and DNA β were amplified from various samples while no amplicon was obtained in some samples. The nucleotide sequence homology ranged from 90.0% to 98.7% with Cotton leaf curl virus (CLCuV), 55.2–55.5% with Bhendi yellow vein mosaic virus, 55.8% with Okra leaf curl virus and 51.70% with Tomato leaf curl virus isolates. The lowest similarity (47.8%) was found in CLCuV-Sudan isolate. Phylogenetic analysis showed that analyzed isolates formed a close cluster with various CLCuV isolates reported earlier. The analysis results show sequence variation in Cotton leaf curl betasatellite which could be the result of recombination. The results obtained by genome amplification and sequence variability indicate that some new variants are circulating and causing leaf curl disease in Rajasthan, Punjab and Haryana.Abbreviations: CLCuD, Cotton leaf curl disease; CLCuV, Cotton leaf curl virus; PCR, polymerase chain reaction; SCR, satellite conserved region     

Modeling recurrent DNA copy number alterations in array CGH data

MOTIVATION: Recurrent DNA copy number alterations (CNA) measured with array comparative genomic hybridization (aCGH) reveal important molecular features of human genetics and disease. Studying aCGH profiles from a phenotypic group of individuals can determine important recurrent CNA patterns that suggest a strong correlation to the phenotype. Computational approaches to detecting recurrent CNAs from a set of aCGH experiments have typically relied on discretizing the noisy log ratios and subsequently inferring patterns. We demonstrate that this can have the effect of filtering out important signals present in the raw data. In this article we develop statistical models that jointly infer CNA patterns and the discrete labels by borrowing statistical strength across samples. RESULTS: We propose extending single sample aCGH HMMs to the multiple sample case in order to infer shared CNAs. We model recurrent CNAs as a profile encoded by a master sequence of states that generates the samples. We show how to improve on two basic models by performing joint inference of the discrete labels and providing sparsity in the output. We demonstrate on synthetic ground truth data and real data from lung cancer cell lines how these two important features of our model improve results over baseline models. We include standard quantitative metrics and a qualitative assessment on which to base our conclusions. AVAILABILITY: http://www.cs.ubc.ca/~sshah/acgh.     

Resolution of sertraline with (R)-mandelic acid: chiral discrimination mechanism study

The chiral discrimination mechanism in the resolution of sertraline with mandelic acid was investigated by examining the weak intermolecular interactions (such as hydrogen bond, CH/π, and van der Waals interactions) and molecular packing difference in crystal structures of the resulting diastereomeric salts. A new one-dimensional chain-like hydrogen-bonding network and unique supramolecular packing mode are disclosed. The investigation demonstrated that stable hydrogen-bonding pattern, herringbone-like arrangement of aromatic rings, and planar boundary surface in the hydrophobic region are the three most important structural characteristics expected in less soluble diastereomeric salts. The existence and magnitude of hydrogen bond, CH/π interaction, and van der Waals interaction related to three characteristic structures, determine the stability of diastereomeric salt. The hydrogen bond is not necessarily the dominant factor while the synergy and optimization of all weak intermolecular interactions attribute to the chiral recognition.