62 research outputs found
Interval reverse nearest neighbor queries on uncertain data with Markov correlations
No full textNowadays, many applications return to the user a set of results that take the query as their nearest neighbor, which are commonly expressed through reverse nearest neighbor (RNN) queries. When considering moving objects, users would like to find objects that appear in the RNN result set for a period of time in some real-world applications such as collaboration recommendation and anti-tracking. In this work, we formally define the problem of interval reverse nearest neighbor (IRNN) queries over moving objects, which return the objects that maintain nearest neighboring relations to the moving query objects for the longest time in the given interval. Location uncertainty of moving data objects and moving query objects is inherent in various domains, and we investigate objects that exhibit Markov correlations, that is, each object's location is only correlated with its own location at previous timestamp while being independent of other objects. There exists the efficiency challenge for answering IRNN queries on uncertain moving objects with Markov correlations since we have to retrieve not only all the possible locations of each object at current time but also its historically possible locations. To speed up the query processing, we present a general framework for answering IRNN queries on uncertain moving objects with Markov correlations in two phases. In the first phase, we apply space pruning and probability pruning techniques, which reduce the search space significantly. In the second phase, we verify whether each unpruned object is an IRNN of the query object. During this phase, we propose an approach termed Probability Decomposition Verification (PDV) algorithm which avoid computing the probability of any object being an RNN of the query object exactly and thus improve the efficiency of verification. The performance of the proposed algorithm is demonstrated by extensive experiments on synthetic and real datasets, and the experimental results show that our algorithm is more efficient than the Monte-Carlo based approximate algorithm.</p
Establishment and validation of a predictive model for moderate and severe respiratory syncytial virus infection in infants
No full textObjective To explore the risk factors for moderate and severe respiratory syncytial virus （RSV） infection in infants， and to establish and validate the predictive model. Methods Clinical data of 399 children with RSV infection were retrospectively analyzed， including 299 cases in the model group and 100 cases in the validation group. Univariate and multivariate Logistic regression analyses were used to screen the risk factors of moderate and severe RSV infection， and a clinical scoring model was established. Results In the model group （n = 299）， 48 children were classified with moderate to severe RSV infection and 251 cases of mild RSV infection. According to univariate and multivariate Logistic regression analyses， body weight， feeding history， wheezing， erythrocyte distribution width and hematocrit were the risk factors （all P < 0.05）， which were used to fit the joint diagnosis and establish the clinical scoring model. The area under the ROC curve （AUC） of clinical scoring model was 0.777 （95%CI 0.703-0.853）， the diagnostic cutoff value was 1.365， the sensitivity was 0.829 and the specificity was 0.604， respectively. The internal validation results showed that the model had high consistency. Conclusion A clinical scoring model for predicting moderate and severe RSV infection is established， which has certain accuracy
A novel technique for trehalose and sucrose determination in therapeutic monoclonal antibodies using a high-performance liquid chromatography–evaporative light scattering detector
No full textSucrose and trehalose are commonly used excipients in therapeutic monoclonal antibodies that play a pivotal role in ensuring the safety and stability of drugs. Though it is necessary to control the concentrations of these substances during the quality control of their release, there is currently no comprehensive method for simultaneously determining sucrose and trehalose concentrations. Herein, we established a high-performance liquid chromatography–evaporative light scattering detector (HPLC-ELSD) method and validated it in accordance with the International Council for Harmonization Q2 guidelines. This method utilized the Poroshell 120 HILIC-Z chromatographic column and effectively separated sucrose and trehalose with a detection limit of 0.001 mg/mL. The accuracy recovery rate was within a range of 90%–110 %, and the precision relative standard deviations were all less than 5.0 % (n = 6). The method thus demonstrated good repeatability and linearity, making it suitable for determining the sucrose and trehalose concentrations in therapeutic monoclonal antibodies
A reporter gene assay for measuring the bioactivity of anti‐LAG‐3 therapeutic antibodies
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Interlaboratory method validation of icIEF methodology for analysis of monoclonal antibodies
No full textFront Cover: Interlaboratory method validation of icIEF methodology for analysis of monoclonal antibodies
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