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Qin, Xiangju, Yang Zhang, Chen Li, and Xue Li. "Learning from data streams with only positive and unlabeled data." Journal of Intelligent Information Systems 40, no. 3 (January 5, 2013): 405–30. http://dx.doi.org/10.1007/s10844-012-0231-6.
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Terada, Yoshikazu, Issei Ogasawara, and Ken Nakata. "Classification from only positive and unlabeled functional data." Annals of Applied Statistics 14, no. 4 (December 2020): 1724–42. http://dx.doi.org/10.1214/20-aoas1404.
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Becerra-Bonache, Leonor. "Learning SECp Languages from Only Positive Data." Triangle, no. 8 (June 29, 2018): 1. http://dx.doi.org/10.17345/triangle8.1-18.
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The eld of Grammatical Inference provides a good theoretical framework for investigating a learning process. Formal results in this eld can be relevant to the question of rst language acquisition. However, Grammatical Inference studies have been focused mainly on mathematical aspects, and have not exploited the linguistic relevance of their results. With this paper, we try to enrich Grammatical Inference studies with ideas from Linguistics. We propose a non-classical mechanism that has relevant linguistic and computational properties, and we study its learnability from positive data.
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Daneshpazhouh, Armin, and Ashkan Sami. "Semi-Supervised Outlier Detection with Only Positive and Unlabeled Data Based on Fuzzy Clustering." International Journal on Artificial Intelligence Tools 24, no. 03 (June 2015): 1550003. http://dx.doi.org/10.1142/s0218213015500037.
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The task of semi-supervised outlier detection is to find the instances that are exceptional from other data, using some labeled examples. In many applications such as fraud detection and intrusion detection, this issue becomes more important. Most existing techniques are unsupervised. On the other hand, semi-supervised approaches use both negative and positive instances to detect outliers. However, in many real world applications, very few positive labeled examples are available. This paper proposes an innovative approach to address this problem. The proposed method works as follows. First, some reliable negative instances are extracted by a kNN-based algorithm. Afterwards, fuzzy clustering using both negative and positive examples is utilized to detect outliers. Experimental results on real data sets demonstrate that the proposed approach outperforms the previous unsupervised state-of-the-art methods in detecting outliers.
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Tomeo, Paolo, Ignacio Fernández-Tobías, Iván Cantador, and Tommaso Di Noia. "Addressing the Cold Start with Positive-Only Feedback Through Semantic-Based Recommendations." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, Suppl. 2 (December 2017): 57–78. http://dx.doi.org/10.1142/s0218488517400116.
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Recommender systems aim to provide users with accurate item suggestions in a personalized fashion, but struggle in the case of cold start users, for whom there is a scarcity of preference data. User preferences can be either explicitly stated by the users — often by means of ratings —, or implicitly acquired by a system — for instance by mining text reviews, search queries, and purchase records. Recommendation methods have been mostly designed to deal with numerical ratings. However, real scenarios with user preferences expressed in the form of binary and unary (positive-only) feedback, e.g. the thumbs up/down in YouTube, and the likes in Facebook, are increasingly popular, and make the user cold start problem even more challenging. To address the cold start with positive-only feedback situations, we propose to exploit data additional to user preferences by means of specialized hybrid recommendation methods. In particular, we investigate a number of graph-based and matrix factorization recommendation models that jointly exploit user preferences and item semantic metadata automatically extracted from the well-known knowledge graph of DBpedia. Following a rigorous evaluation methodology for cold start, we empirically compare the above hybrid recommendation models on a Facebook dataset containing users likes for items in three different domains, namely books, movies and music. The achieved experimental results show that the semantics-aware hybrid approaches we propose outperform content-based and collaborative filtering baselines. In addition to recommendation accuracy, in our evaluation we also consider individual and aggregate diversity of recommendations as key quality factors in the users’ satisfaction.
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Leibowitz, Arleen A., and Katherine Desmond. "Do Only 21% of HIV-Positive Medicaid Enrollees Link to Treatment? Challenges in Interpreting Medicaid Claims Data." Sexually Transmitted Diseases 40, no. 7 (July 2013): 582. http://dx.doi.org/10.1097/01.olq.0000430802.91969.98.
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Cheng, Zhanzhan, Shuigeng Zhou, and Jihong Guan. "Computationally predicting protein-RNA interactions using only positive and unlabeled examples." Journal of Bioinformatics and Computational Biology 13, no. 03 (May 15, 2015): 1541005. http://dx.doi.org/10.1142/s021972001541005x.
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Protein–RNA interactions (PRIs) are considerably important in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulations of gene expression to the active defense of host against virus. With the development of high throughput technology, large amounts of PRI information is available for computationally predicting unknown PRIs. In recent years, a number of computational methods for predicting PRIs have been developed in the literature, which usually artificially construct negative samples based on verified nonredundant datasets of PRIs to train classifiers. However, such negative samples are not real negative samples, some even may be unknown positive samples. Consequently, the classifiers trained with such training datasets cannot achieve satisfactory prediction performance. In this paper, we propose a novel method PRIPU that employs biased-support vector machine (SVM) for predicting Protein-RNA Interactions using only Positive and Unlabeled examples. To the best of our knowledge, this is the first work that predicts PRIs using only positive and unlabeled samples. We first collect known PRIs as our benchmark datasets and extract sequence-based features to represent each PRI. To reduce the dimension of feature vectors for lowering computational cost, we select a subset of features by a filter-based feature selection method. Then, biased-SVM is employed to train prediction models with different PRI datasets. To evaluate the new method, we also propose a new performance measure called explicit positive recall (EPR), which is specifically suitable for the task of learning positive and unlabeled data. Experimental results over three datasets show that our method not only outperforms four existing methods, but also is able to predict unknown PRIs. Source code, datasets and related documents of PRIPU are available at: http://admis.fudan.edu.cn/projects/pripu.htm .
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Kamaludin, Hazalila, Hairulnizam Mahdin, and Jemal H. Abawajy. "Filtering Redundant Data from RFID Data Streams." Journal of Sensors 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/7107914.
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Radio Frequency Identification (RFID) enabled systems are evolving in many applications that need to know the physical location of objects such as supply chain management. Naturally, RFID systems create large volumes of duplicate data. As the duplicate data wastes communication, processing, and storage resources as well as delaying decision-making, filtering duplicate data from RFID data stream is an important and challenging problem. Existing Bloom Filter-based approaches for filtering duplicate RFID data streams are complex and slow as they use multiple hash functions. In this paper, we propose an approach for filtering duplicate data from RFID data streams. The proposed approach is based on modified Bloom Filter and uses only a single hash function. We performed extensive empirical study of the proposed approach and compared it against the Bloom Filter, d-Left Time Bloom Filter, and the Count Bloom Filter approaches. The results show that the proposed approach outperforms the baseline approaches in terms of false positive rate, execution time, and true positive rate.
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KOBAYASHI, SATOSHI, and TAKASHI YOKOMORI. "FAMILIES OF NONCOUNTING LANGUAGES AND THEIR LEARNABILITY FROM POSITIVE DATA." International Journal of Foundations of Computer Science 07, no. 04 (December 1996): 309–27. http://dx.doi.org/10.1142/s0129054196000221.
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This paper introduces some subclasses of noncounting languages and presents some results on the learnability of the classes from positive data. We first establish several relationships among the language classes introduced and the class of reversible languages. Especially, we introduce the notion of local parsability, and define a class (k, l)-CLTS, which is a subclass of the class of concatenations of strictly locally testable languages. We show its close relation to the class of reversible languages. We then study on the relationship between the closure of the Boolean operations and the learnability in the limit from positive data only. Further, we explore the learnability question of some subclasses of noncounting languages in the model of identification in the limit from positive data. In particular, we show that, for each k, l≥1, (k, l)-CLTS is identifiable in the limit from positive data using reversible automata with the conjectures updated in polynomial time. Some possible applications of the result are also briefly discussed.
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Sakai, Tomoya, and Nobuyuki Shimizu. "Covariate Shift Adaptation on Learning from Positive and Unlabeled Data." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4838–45. http://dx.doi.org/10.1609/aaai.v33i01.33014838.
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The goal of binary classification is to identify whether an input sample belongs to positive or negative classes. Usually, supervised learning is applied to obtain a classification rule, but in real-world applications, it is conceivable that only positive and unlabeled data are accessible for learning, which is called learning from positive and unlabeled data (PU learning). Furthermore, in practice, data distributions are likely to differ between training and testing due to, for example, time variation and domain shift. The covariate shift is a dataset shift situation, where distributions of covariates (inputs) differ between training and testing, but the input-output relation is the same. In this paper, we address the PU learning problem under the covariate shift. We propose an importanceweighted PU learning method and reveal in which situations the importance-weighting is necessary. Moreover, we derive the convergence rate of the proposed method under mild conditions and experimentally demonstrate its effectiveness.
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Anderson, Joseph C., Paul Limburg, Christina Robinson, William Hisey, and Lynn Butterly. "ID: 3517867 COMPARISON OF COLONOSCOPY FINDINGS IN PATIENTS WITH PRECEDING FIT POSITIVE AND MT-SDNA POSITIVE TESTS TO PATIENTS HAVING A COLONOSCOPY ONLY: DATA FROM THE NEW HAMPSHIRE COLONOSCOPY REGISTRY." Gastrointestinal Endoscopy 93, no. 6 (June 2021): AB34—AB35. http://dx.doi.org/10.1016/j.gie.2021.03.133.
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LONGACRE, ANGELIKA, L. RIDGWAY SCOTT, and JERROLD S. LEVINE. "LINEAR INDEPENDENCE OF PAIRWISE COMPARISONS OF DNA MICROARRAY DATA." Journal of Bioinformatics and Computational Biology 03, no. 06 (December 2005): 1243–62. http://dx.doi.org/10.1142/s0219720005001600.
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Motivation: For DNA microarrays, the gain in certainty by performing multiple experimental repeats is offset by the high cost of each experiment. In a typical experiment, two independent measurements (that is, data from two separate arrays) are combined to yield a single comparative index per gene. Thus, although one uses 2n arrays and performs 2n independent measurements, one obtains only n comparative measurements. We addressed the question: how many of the potential n2 comparisons derivable from such data are actually independent, and what effect do these additional comparisons have on the false positive rate. Results: We show there are precisely 2n - 1 independent comparisons available from among the n2 possibilities. Applying these additional n - 1 independent comparisons to experimental and simulated data reduced the false positive rate by as much as 10-fold, with excellent agreement between experimental and theoretical false positive rates.
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Stock, Eileen M., James D. Stamey, Rengaswamy Sankaranarayanan, Dean M. Young, Richard Muwonge, and Marc Arbyn. "Estimation of disease prevalence, true positive rate, and false positive rate of two screening tests when disease verification is applied on only screen-positives: A hierarchical model using multi-center data." Cancer Epidemiology 36, no. 2 (April 2012): 153–60. http://dx.doi.org/10.1016/j.canep.2011.07.001.
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Teoh, Siew Hong, and Yinglei Zhang. "Data Truncation Bias, Loss Firms, and Accounting Anomalies." Accounting Review 86, no. 4 (April 1, 2011): 1445–75. http://dx.doi.org/10.2308/accr-10032.
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ABSTRACT Ex post trimming of extreme returns observations that are not data errors causes spurious inferences in tests of market efficiency and behavioral explanations for anomalies. Trimming causes a downward truncation bias in estimated mean returns that is stronger in ex ante subsamples with more loss firms and in which return distributions are more right-skewed. There is an asymmetric U-shaped relation between return right-skewness and loss frequency across deciles of negative return predictors (Accruals, ΔNOA, and NOA), and a downward sloping relationship for positive return predictors (CFO and FCF). Consequently, a least-trimmed square (LTS) 1 percent deletion of returns induces a spurious inverted-U-shaped relation between returns and negative predictors, and an exaggerated positive relation for positive predictors. Thus, the resulting trimmed relations do not reject behavioral explanations for these anomalies. Trimming also induces a spurious loss anomaly. These findings highlight that in return prediction studies, observations should not be deleted based upon the values of the dependent variable, only based upon clearly identified data errors.
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Ghiyasi, Mojtaba, and Ning Zhu. "An inverse semi-oriented radial data envelopment analysis measure for dealing with negative data." IMA Journal of Management Mathematics 31, no. 4 (April 29, 2020): 505–16. http://dx.doi.org/10.1093/imaman/dpaa007.
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Abstract The conventional inverse data envelopment analysis (DEA) model is only applicable to positive data, while negative data are commonly present in most real-world applications. This paper proposes a novel inverse DEA model that can handle negative data. The conventional inverse DEA model is a special case of our model as our model is more general in terms of returns-to-scale properties. The proposed model is used to evaluate the efficiency of the Chinese commercial banks after the global financial crisis, where negative outputs existed. We show that our model is feasible in the presence of negative data and generates empirical findings that are consistent with reality.
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Jiang, Xiaoqi, Tiantian Xu, and Xiangjun Dong. "Campus Data Analysis Based on Positive and Negative Sequential Patterns." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 05 (April 8, 2019): 1959016. http://dx.doi.org/10.1142/s021800141959016x.
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Campus data analysis is becoming increasingly important in mining students’ behavior. The consumption data of college students is an important part of the campus data, which can reflect the students’ behavior to a great degree. A few methods have been used to analyze students’ consumption data, such as classification, association rules, clustering, decision trees, time series, etc. However, they do not use the method of sequential patterns mining, which results in some important information missing. Moreover, they only consider the occurring (positive) events but do not consider the nonoccurring (negative) events, which may lead to some important information missing. So this paper uses a positive and negative sequential patterns mining algorithm, called NegI-NSP, to analyze the consumption data of students. Moreover, we associate students’ consumption data with their academic grades by adding the students’ academic grades into sequences to analyze the relationship between the students’ academic grades and their consumptions. The experimental results show that the students’ academic performance has significant correlation with the habits of having breakfast regularly.
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Lorey, Fred. "Human Genetics Data Applied to Genetic Screening Programs." Practicing Anthropology 20, no. 2 (April 1, 1998): 30–33. http://dx.doi.org/10.17730/praa.20.2.n84728r821185380.
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The uses of human genetic data in genetic screening are multifaceted and dynamic, creating an ongoing stream of useful prevalence data, ethnicity data, and natural history information. Since the primary facility for generation of these data is a large public health genetic screening program, however, the results must be continually analyzed and evaluated in the context of testing parameters. For example, presumptive positive rates (initial screening test positives, only a portion of which will become diagnosed cases), false positive rates, detection rates, and analytical values must be constantly checked to ensure the screening program is running smoothly and effectively. Any departures from the expected must be investigated so that the cause(s) can be determined and corrected. On a longitudinal basis, outcomes must be evaluated to ensure that the intended purpose of preventing mortality and reducing morbidity through intervention is achieved.
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Gawne, Lauren, Chelsea Krajcik, Helene N. Andreassen, Andrea L. Berez-Kroeker, and Barbara F. Kelly. "Data transparency and citation in the journal Gesture." Gesture 18, no. 1 (December 31, 2019): 83–109. http://dx.doi.org/10.1075/gest.00034.gaw.
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Abstract Data is central to scholarly research, but the nature and location of data used is often under-reported in research publications. Greater transparency and citation of data have positive effects for the culture of research. This article presents the results of a survey of data citation in six years of articles published in the journal Gesture (12.1–17.2). Gesture researchers draw on a broad range of data types, but the source and location of data are often not disclosed in publications. There is also still a strong research focus on only a small range of the world’s languages and their linguistic diversity. Published papers rarely cite back to the primary data, unless it is already published. We discuss both the implications of these findings and the ways that scholars in the field of gesture studies can build a positive culture around open data.
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Fang*, Wei, Yang Zheng*, Fan Yang, Meng-Ting Cai, Chun-Hong Shen, Zhi-Rong Liu, Yin-Xi Zhang, and Mei-Ping Ding. "Short segment myelitis as the initial and only manifestation of aquaporin-4 immunoglobulin G-positive neuromyelitis optica spectrum disorders." Therapeutic Advances in Neurological Disorders 13 (January 2020): 175628641989859. http://dx.doi.org/10.1177/1756286419898594.
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Background: Short segment myelitis (SSM, < 3 vertebral segments) is an under-recognized initial manifestation of neuromyelitis optica spectrum disorders (NMOSD). Though infrequent, failure to recognize SSM in patients with NMOSD would lead to incorrect diagnosis and treatment. Therefore, delineation of features of NMOSD-associated SSM is of paramount importance. Objective: Our study aimed to determine the demographic, clinical and radiological features of NMOSD-associated SSM, and compare those with NMOSD-associated longitudinally extensive transverse myelitis (LETM) and multiple sclerosis (MS)-associated SSM, respectively. Methods: Chinese patients presenting initially only with acute myelitis and diagnosed with NMOSD ( n = 46) and MS ( n = 11) were included. Clinical, serological, imaging and disability data were collected. Mann–Whitney U test or two-tailed Fisher’s exact tests were used to analyse the data. Results: Of the 46 enrolled NMOSD patients, 34 (74%) collectively had 38 LETM lesions, while 12 (26%) had 14 SSM lesions. When compared with LETM, NMOSD presenting with SSM were more likely to have a delayed diagnosis and a lower level of disability at nadir during the first attack. T1-weighted imaging hypointensity was more prominent in NMOSD-associated LETM lesions than NMOSD-associated SSM lesions. When compared with MS-associated SSM, NMOSD-associated SSM lesions were more likely to be centrally located, grey matter involving and transversally extensive on axial imaging and spanned no less than 2 vertebral segments on sagittal imaging. Conclusion: These findings suggest that SSM does not preclude the possibility of a NMOSD diagnosis. Testing for serum aquaporin-4 immunoglobulin G (AQP4-IgG) and careful study of lesions on spinal cord magnetic resonance imaging could aid in an earlier and correct diagnosis.
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Wang, Siye, Ziwen Cao, Yanfang Zhang, Weiqing Huang, and Jianguo Jiang. "A Temporal and Spatial Data Redundancy Processing Algorithm for RFID Surveillance Data." Wireless Communications and Mobile Computing 2020 (February 24, 2020): 1–12. http://dx.doi.org/10.1155/2020/6937912.
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The Radio Frequency Identification (RFID) data acquisition rate used for monitoring is so high that the RFID data stream contains a large amount of redundant data, which increases the system overhead. To balance the accuracy and real-time performance of monitoring, it is necessary to filter out redundant RFID data. We propose an algorithm called Time-Distance Bloom Filter (TDBF) that takes into account the read time and read distance of RFID tags, which greatly reduces data redundancy. In addition, we have proposed a measurement of the filter performance evaluation indicators. In experiments, we found that the performance score of the TDBF algorithm was 5.2, while the Time Bloom Filter (TBF) score was only 0.03, which indicates that the TDBF algorithm can achieve a lower false negative rate, lower false positive rate, and higher data compression rate. Furthermore, in a dynamic scenario, the TDBF algorithm can filter out valid data according to the actual scenario requirements.
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Shield, Jennifer, Sabine Braat, Matthew Watts, Gemma Robertson, Miles Beaman, James McLeod, Robert W. Baird, et al. "Seropositivity and geographical distribution of Strongyloides stercoralis in Australia: A study of pathology laboratory data from 2012–2016." PLOS Neglected Tropical Diseases 15, no. 3 (March 9, 2021): e0009160. http://dx.doi.org/10.1371/journal.pntd.0009160.
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Background There are no national prevalence studies of Strongyloides stercoralis infection in Australia, although it is known to be endemic in northern Australia and is reported in high risk groups such as immigrants and returned travellers. We aimed to determine the seropositivity (number positive per 100,000 of population and percent positive of those tested) and geographical distribution of S. stercoralis by using data from pathology laboratories. Methodology We contacted all seven Australian laboratories that undertake Strongyloides serological (ELISA antibody) testing to request de-identified data from 2012–2016 inclusive. Six responded. One provided positive data only. The number of people positive, number negative and number tested per 100,000 of population (Australian Bureau of Statistics data) were calculated including for each state/territory, each Australian Bureau of Statistics Statistical Area Level 3 (region), and each suburb/town/community/locality. The data was summarized and expressed as maps of Australia and Greater Capital Cities. Principal findings We obtained data for 81,777 people who underwent serological testing for Strongyloides infection, 631 of whom were from a laboratory that provided positive data only. Overall, 32 (95% CI: 31, 33) people per 100,000 of population were seropositive, ranging between 23/100,000 (95% CI: 19, 29) (Tasmania) and 489/100,000 population (95%CI: 462, 517) (Northern Territory). Positive cases were detected across all states and territories, with the highest (260-996/100,000 and 17–40% of those tested) in regions across northern Australia, north-east New South Wales and north-west South Australia. Some regions in Greater Capital Cities also had a high seropositivity (112-188/100,000 and 17–20% of those tested). Relatively more males than females tested positive. Relatively more adults than children tested positive. Children were under-represented in the data. Conclusions/Significance The study confirms that substantial numbers of S. stercoralis infections occur in Australia and provides data to inform public health planning.
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Zhao, Xiangyu, Zhendong Niu, Kaiyi Wang, Ke Niu, and Zhongqiang Liu. "Improving Top-NRecommendation Performance Using Missing Data." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/380472.
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Recommender systems become increasingly significant in solving the information explosion problem. Data sparse is a main challenge in this area. Massive unrated items constitute missing data with only a few observed ratings. Most studies consider missing data as unknown information and only use observed data to learn models and generate recommendations. However, data are missing not at random. Part of missing data is due to the fact that users choose not to rate them. This part of missing data is negative examples of user preferences. Utilizing this information is expected to leverage the performance of recommendation algorithms. Unfortunately, negative examples are mixed with unlabeled positive examples in missing data, and they are hard to be distinguished. In this paper, we propose three schemes to utilize the negative examples in missing data. The schemes are then adapted with SVD++, which is a state-of-the-art matrix factorization recommendation approach, to generate recommendations. Experimental results on two real datasets show that our proposed approaches gain better top-Nperformance than the baseline ones on both accuracy and diversity.
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Duan, Xiao Gang, and Feng Gao. "Application of Data Mining on Stock Market." Applied Mechanics and Materials 513-517 (February 2014): 1352–55. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1352.
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Forecasting the stock market price movements is now popular in the field of financial research. A large number of scholars has carried on the positive exploration. Only these people are more focused on selection of prediction methods and algorithm optimization. In view of the stock market time series has the nature of the multi-scale features, nonstationary and nonlinear properties and low signal-to-noise ratio of some different from other general characteristics of time series, this paper puts forward building a multi-scale technique index method for preprocessing of the input data and then used very popular in recent years the output of the neural network technology to the pre-processed data to make predictions.
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Ryabko, Boris. "Time-Universal Data Compression." Algorithms 12, no. 6 (May 29, 2019): 116. http://dx.doi.org/10.3390/a12060116.
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Nowadays, a variety of data-compressors (or archivers) is available, each of which has its merits, and it is impossible to single out the best ones. Thus, one faces the problem of choosing the best method to compress a given file, and this problem is more important the larger is the file. It seems natural to try all the compressors and then choose the one that gives the shortest compressed file, then transfer (or store) the index number of the best compressor (it requires log m bits, if m is the number of compressors available) and the compressed file. The only problem is the time, which essentially increases due to the need to compress the file m times (in order to find the best compressor). We suggest a method of data compression whose performance is close to optimal, but for which the extra time needed is relatively small: the ratio of this extra time and the total time of calculation can be limited, in an asymptotic manner, by an arbitrary positive constant. In short, the main idea of the suggested approach is as follows: in order to find the best, try all the data compressors, but, when doing so, use for compression only a small part of the file. Then apply the best data compressors to the whole file. Note that there are many situations where it may be necessary to find the best data compressor out of a given set. In such a case, it is often done by comparing compressors empirically. One of the goals of this work is to turn such a selection process into a part of the data compression method, automating and optimizing it.
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Vilar, Polona, and Vlasta Zabukovec. "Research data management and research data literacy in Slovenian science." Journal of Documentation 75, no. 1 (January 14, 2019): 24–43. http://dx.doi.org/10.1108/jd-03-2018-0042.
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PurposeThe purpose of this paper is to investigate the differences between scientific disciplines (SDs) in Slovenia in research data literacy (RDL) and research data management (RDM) to form recommendations regarding how to move things forward on the institutional and national level.Design/methodology/approachPurposive sample of active researchers was used from widest possible range of SD. Data were collected from April 21 to August 7, 2017, using 24-question online survey (5 demographic, 19 content questions (single/multiple choice and Likert scale type). Bivariate (ANOVA) and multivariate methods (clustering) were used.FindingsThe authors identified three perception-related and four behavior-related connections; this gave three clusters per area. First, perceptions – skeptical group, mainly social (SocS) and natural sciences (NatS): no clear RDM and ethical issues standpoints, do not agree that every university needs a data management plan (DMP). Careful group, again including mainly SocS and NatS: RDM is problematic and linked to ethical dilemmas, positive toward institutional DMPs. Convinced group, mainly from humanities (HUM), NatS, engineering (ENG) and medicine and health sciences (MedHeS): no problems regarding RDM, agrees this is an ethical question, is positive toward institutional DMP’s. Second, behaviors – sparse group, mainly from MedHeS, NatS and HUM, some agricultural scientists (AgS), and some SocS and ENG: do not tag data sets with metadata, do not use file-naming conventions/standards. Frequent group – many ENG, SocS, moderate numbers of NatS, very few AgS and only a few MedHeS and HUM: often use file-naming conventions/standards, version-control systems, have experience with public-domain data, are reluctant to use metadata with their RD. Slender group, mainly from AgS and NatS, moderate numbers of ENG, SocS and HUM, but no MedHeS: often use public-domain data, other three activities are rare.Research limitations/implicationsResearch could be expanded to a wider population, include other stakeholders and use qualitative methods.Practical implicationsResults are useful for international comparisons but also give foundations and recommendations on institutional and national RDM and RDL policies, implementations, and how to bring academic libraries into the picture. Identified differences suggest that different educational, awareness-raising and participatory approaches are needed for each group.Originality/valueThe findings offer valuable insight into RDM and RDL of Slovenian scientists, which have not yet been investigated in Slovenia.
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Polak, Jonathan, Ogheneruona Odili, Mary Ashleigh Craver, Anthony Mayen, Kyle Purrman, Asem Rahman, Charlie Joseph Sang, and Paul P. Cook. "Clostridium difficile Testing Algorithm: Is There a Difference in Patients Who Test Positive by Enzyme Immunoassay vs. Those Who Only Test Positive by Nucleic Acid Amplification Methodology?" Open Forum Infectious Diseases 4, suppl_1 (2017): S394. http://dx.doi.org/10.1093/ofid/ofx163.981.
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Abstract Background Testing for Clostridium difficile infection (CDI) commonly involves checking for the presence of toxins A and B by enzyme immunoassay (EIA) or nucleic acid amplification (NAA). The former is very specific, but not very sensitive. The latter is very sensitive. Beginning in 2011, our hospital incorporated an algorithm that involved testing liquid stool specimens for glutamate dehydrogenase (GDH) and toxin by EIA. For discrepant results, the stool specimen was tested for the presence of toxin by NAA. We sought to determine whether there was a difference in the baseline characteristics or outcomes between the two groups. Methods We performed a chart review of all subjects who tested positive for CDI by either method between 2011 and 2016 at Vidant Medical Center, a 909 bed, tertiary care teaching hospital. Testing was only performed on liquid stool specimens. Subjects less than 18 years of age were excluded. Repeat positive specimens were excluded. We collected demographic data including age, gender, baseline temperature, white blood cell count, and serum lactate and albumin. Length of stay and in-hospital mortality were also determined for both groups. Comparison of the two groups was done using t-test for continuous and chi-square analysis for categorical variables. Results Over the 6 year period, there were 535 positive test results. 243 specimens tested positive by EIA/GDH (EIA +); 292 specimens tested positive by GDH/NAA (NAA +). Compared with the EIA + group, the NAA + group was younger (61.8 years vs. 65.1 years, P = 0.01). There were no statistical differences in the presence of abdominal tenderness, temperature >38oC, serum albumin, serum lactate, length of stay, or mortality between the two groups. The EIA + group was statistically more likely to have leukocytosis (WBC >20,000 cells/mm3) at the time of the CDI testing compared with the NAA + group (P = 0.0002). Conclusion There do appear to be some clinical differences in the presentation of subjects who test positive for CDI by EIA/GDH compared with those who test positive only by GDH/NAA. These differences do not appear to affect length of stay or mortality. Disclosures P. P. Cook, Gilead: Grant Investigator, Grant recipient; Merck: Grant Investigator, Grant recipient; Pfizer: Grant Investigator and Shareholder, Grant recipient
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Tontini, Fabio Caratori, Osvaldo Faggioni, Nicolò Beverini, and Cosmo Carmisciano. "Gaussian envelope for 3D geomagnetic data inversion." GEOPHYSICS 68, no. 3 (May 2003): 996–1007. http://dx.doi.org/10.1190/1.1581071.
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We describe an inversion method for 3D geomagnetic data based on approximation of the source distribution by means of positive constrained Gaussian functions. In this way, smoothness and positivity are automatically imposed on the source without any subjective input from the user apart from selecting the number of functions to use. The algorithm has been tested with synthetic data in order to resolve sources at very different depths, using data from one measurement plane only. The forward modeling is based on prismatic cell parameterization, but the algebraic nonuniqueness is reduced because a relationship among the cells, expressed by the Gaussian envelope, is assumed to describe the spatial variation of the source distribution. We assume that there is no remanent magnetization and that the magnetic data are produced by induced magnetization only, neglecting any demagnetization effects. The algorithm proceeds by minimization of a χ2 misfit function between real and predicted data using a nonlinear Levenberg‐Marquardt iteration scheme, easily implemented on a desktop PC, without any additional regularization. We demonstrate the robustness and utility of the method using synthetic data corrupted by pseudorandom generated noise and a real field data set.
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KALININ, BORIS, and VICTORIA SADOVSKAYA. "On Anosov diffeomorphisms with asymptotically conformal periodic data." Ergodic Theory and Dynamical Systems 29, no. 1 (February 2009): 117–36. http://dx.doi.org/10.1017/s0143385708000357.
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AbstractWe consider transitive Anosov diffeomorphisms for which every periodic orbit has only one positive and one negative Lyapunov exponent. We prove various properties of such systems, including strong pinching, C1+β smoothness of the Anosov splitting, and C1 smoothness of measurable invariant conformal structures and distributions. We apply these results to volume-preserving diffeomorphisms with two-dimensional stable and unstable distributions and diagonalizable derivatives of the return maps at periodic points. We show that a finite cover of such a diffeomorphism is smoothly conjugate to an Anosov automorphism of 𝕋4; as a corollary, we obtain local rigidity for such diffeomorphisms. We also establish a local rigidity result for Anosov diffeomorphisms in dimension three.
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Jaskie, Kristen, Joshua Martin, and Andreas Spanias. "PV Fault Detection Using Positive Unlabeled Learning." Applied Sciences 11, no. 12 (June 17, 2021): 5599. http://dx.doi.org/10.3390/app11125599.
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Solar array management and photovoltaic (PV) fault detection is critical for optimal and robust performance of solar plants. PV faults cause substantial power reduction along with health and fire hazards. Traditional machine learning solutions require large, labeled datasets which are often expensive and/or difficult to obtain. This data can be location and sensor specific, noisy, and resource intensive. In this paper, we develop and demonstrate new semi supervised solutions for PV fault detection. More specifically, we demonstrate that a little-known area of semi-supervised machine learning called positive unlabeled learning can effectively learn solar fault detection models using only a fraction of the labeled data required by traditional techniques. We further introduce a new feedback enhanced positive unlabeled learning algorithm that can increase model accuracy and performance in situations such as solar fault detection when few sensor features are available. Using these algorithms, we create a positive unlabeled solar fault detection model that can match and even exceed the performance of a fully supervised fault classifier using only 5% of the total labeled data.
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Kaari, Jennifer. "Researchers at Arab Universities Hold Positive Views on Research Data Management and Data Sharing." Evidence Based Library and Information Practice 15, no. 2 (June 15, 2020): 168–70. http://dx.doi.org/10.18438/eblip29746.
Full textAbstract:
A Review of:
Elsayed, A. M., & Saleh, E. I. (2018). Research data management and sharing among researchers in Arab universities: An exploratory study. IFLA Journal, 44(4), 281–299. https://doi.org/10.1177/0340035218785196
Abstract
Objective – To investigate researchers’ practices and attitudes regarding research data management and data sharing.
Design – Email survey.
Setting – Universities in Egypt, Jordan, and Saudi Arabia.
Subjects – Surveys were sent to 4,086 academic faculty researchers.
Methods – The survey was emailed to faculty at three Arab universities, targeting faculty in the life sciences and engineering. The survey was created using Google Docs and remained open for five months. Participants were asked basic demographic questions, questions regarding their research data and metadata practices, and questions regarding their data sharing practices.
Main Results – The authors received 337 responses, for a response rate of 8%. The results showed that 48.4% of respondents had a data management plan and that 97% were responsible for preserving their own data. Most respondents stored their research data on their personal storage devices. The authors found that 64.4% of respondents reported sharing their research data. Respondents most frequently shared their data by publishing in a data research journal, sharing through academic social networks such as ResearchGate, and providing data upon request to peers. Only 5.1% of respondents shared data through an open data repository. Of those who did not share data, data privacy and confidentiality were the most common reasons cited. Of the respondents who did share their data, contributing to scientific progress and increased citation and visibility were the primary reasons for doing so. A total of 59.6% of respondents stated that they needed more training in research data management from their universities.
Conclusion – The authors conclude that researchers at Arab universities are still primarily responsible for their own data and that data management planning is still a new concept to most researchers. For the most part, the researchers had a positive attitude toward data sharing, although depositing data in open repositories is still not a widespread practice. The authors conclude that in order to encourage strong data management practices and open data sharing among Arab university researchers, more training and institutional support is needed.
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Ahmad, Ijaz, De Shan Tang, Mei Wang, and Sarfraz Hashim. "Trend Analysis on Precipitation Time Series Data in Munda Catchment, Pakistan." Applied Mechanics and Materials 692 (November 2014): 97–102. http://dx.doi.org/10.4028/www.scientific.net/amm.692.97.
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This paper investigates the trends in precipitation time series of 10 stations for the time period of 51 years (1961-2011) in the Munda catchment, Pakistan. The Mann-Kendall (MK) and Spearman’s rho (SR) tests were employed for detection of the trend on the seasonal and annual basis at 5% significance level. For the removal of the serial correlation Trend Free Pre-Whitening approach was applied. The results show, a mixture of positive (increasing) and negative (decreasing) trends. A shift in precipitation time series is observed on seasonal scale from summer to autumn season. The Charbagh station exhibits the most number of significant cases on the seasonal basis while, no significant trends are found at Thalozom, Kalam and Dir stations. On the annual basis, only Charbagh station shows a significant positive trend, while on other stations, no significant trends are found annually. The performance of MK and SR tests was consistent in detecting the trend at different stations.
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Quinquenel, Anne, Chadi Al Nawakil, Fanny Baran-Marszak, Virginie Eclache, Remi Letestu, Christine Le Roy, Nadine Varin-Blank, Alain Delmer, Vincent Levy, and Florence Cymbalista. "Old DAT / New Data : Positive Direct Antiglobulin Test Identifies a Specific Subgroup Of CLL Patients." Blood 122, no. 21 (November 15, 2013): 4154. http://dx.doi.org/10.1182/blood.v122.21.4154.4154.
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Abstract Autoimmune cytopenias represent a well-known complication of chronic lymphocytic leukemia (CLL). The most frequent one is autoimmune haemolytic anemia (AIHA), occurring in about 5% of the patients during the course of the disease. The prognostic significance of autoimmune cytopenias remains uncertain and controversial. In previous studies, a positive direct antiglobulin test (DAT) was found in up to 35% of CLL cases, however only a minority of the patients would develop AIHA at any time of the disease. The aim of this monocentric study was to explore the clinical and biological characteristics and the outcome of CLL patients showing a positive direct antiglobulin test at any time during the course of the disease. We reviewed all CLL patients seen at our institution who had at least one positive DAT between January 2007 and May 2013. Fifty-four patients were found, representing about 10% of our CLL cohort. The sex ratio was 1,8 (35M/19F). Median age at diagnosis was 66,2 years (range 44,7 to 87,3). According to the Binet classification, 41 (76 %) patients were in stage A, 7 in stage B, and 6 in stage C. Study of usual prognostic parameters evidenced that these patients represented a very high risk group: 82% of cases harbored unmutated IGHV, and CD38 was expressed in 60% cases. Cytogenetic alterations were studied by FISH analysis: 11q deletion, trisomy 12 and 17p deletion were found in 12,5%, 22,9% and 14,3% cases respectively. In this cohort, with a median follow up of 85 months, 41 (76 %) patients required treatment. The first line of treatment was initiated for CLL progression in 33 cases, for symptomatic AIHA along with CLL progression in 5, and for symptomatic AIHA (without CLL progression) in 3. The median time to first treatment was very short (16 months), and the median overall survival of this cohort was 84 months. In 22 cases, the DAT was positive from the time of diagnosis, while in 34 patients it became positive later during the course of CLL. Only 19 patients (35%) developed symptomatic AIHA. DAT specificity was IgG, IgG+ C or C for respectively 36, 10 and 8 patients. Interestingly, there was no impact of the time of first positive DAT during the course of the disease (at diagnosis or later). Moreover, occurrence of a symptomatic AIHA had no impact on treatment free survival or overall survival when compared with cases with positive DAT only. Among these cases, 41 were in stage A at diagnosis and up to 78% harbored unmutated IGHV (as compared with 30 % in our institutional stage cohort). In order to evaluate the impact of a positive DAT on a homogenous population, we focused on the 31 IGHV unmutated stage A cases and compared them with our cohort of IGHV unmutated stage A with consistently negative DAT. We found a significant prevalence of VH1-69 and VH 3-21 use (43 %) and a high percentage belonging to a stereotyped subset (34%). The majority of the cases (19/28 tested) were responders to in vitro B-cell receptor stimulation by anti IgM. In this population, time to first treatment (26 months) was slightly shorter but median overall survival (55 months) was significantly reduced highlighting the poor response to treatment. In conclusion, occurrence of a positive DAT at any time of the course of CLL is associated with poor outcome and has the same adverse prognostic impact as the onset of a symptomatic AIHA. Moreover, study of B-Cell receptor (BCR stimulation by anti-IgM, CDR3 stereotypy) show that these patients may represent a pathophysiological distinct subgroup in which antigen stimulation is likely to play a major role. Disclosures: No relevant conflicts of interest to declare.
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Lam, Son K., Stefan Sleep, Thorsten Hennig-Thurau, Shrihari Sridhar, and Alok R. Saboo. "Leveraging Frontline Employees’ Small Data and Firm-Level Big Data in Frontline Management." Journal of Service Research 20, no. 1 (November 18, 2016): 12–28. http://dx.doi.org/10.1177/1094670516679271.
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The advent of new forms of data, modern technology, and advanced data analytics offer service providers both opportunities and risks. This article builds on the phenomenon of big data and offers an integrative conceptual framework that captures not only the benefits but also the costs of big data for managing the frontline employee (FLE)-customer interaction. Along the positive path, the framework explains how the “3Vs” of big data (volume, velocity, and variety) have the potential to improve service quality and reduce service costs by influencing big data value and organizational change at the firm and FLE levels. However, the 3Vs of big data also increase big data veracity, which casts doubt about the value of big data. The authors further propose that because of heterogeneity in big data absorptive capacities at the firm level, the costs of adopting big data in FLE management may outweigh the benefits. Finally, while FLEs can benefit from big data, extracting knowledge from such data does not discount knowledge derived from FLEs’ small data. Rather, combining and integrating the firm’s big data with FLEs’ small data are crucial to absorbing and applying big data knowledge. An agenda for future research concludes.
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Shofiya, Carol, and Samina Abidi. "Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data." International Journal of Environmental Research and Public Health 18, no. 11 (June 3, 2021): 5993. http://dx.doi.org/10.3390/ijerph18115993.
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Background: COVID-19 preventive measures have been an obstacle to millions of people around the world, influencing not only their normal day-to-day activities but also affecting their mental health. Social distancing is one such preventive measure. People express their opinions freely through social media platforms like Twitter, which can be shared among other users. The articulated texts from Twitter can be analyzed to find the sentiments of the public concerning social distancing. Objective: To understand and analyze public sentiments towards social distancing as articulated in Twitter textual data. Methods: Twitter data specific to Canada and texts comprising social distancing keywords were extrapolated, followed by utilizing the SentiStrength tool to extricate sentiment polarity of tweet texts. Thereafter, the support vector machine (SVM) algorithm was employed for sentiment classification. Evaluation of performance was measured with a confusion matrix, precision, recall, and F1 measure. Results: This study resulted in the extraction of a total of 629 tweet texts, of which, 40% of tweets exhibited neutral sentiments, followed by 35% of tweets showed negative sentiments and only 25% of tweets expressed positive sentiments towards social distancing. The SVM algorithm was applied by dissecting the dataset into 80% training and 20% testing data. Performance evaluation resulted in an accuracy of 71%. Upon using tweet texts with only positive and negative sentiment polarity, the accuracy increased to 81%. It was observed that reducing test data by 10% increased the accuracy to 87%. Conclusion: Results showed that an increase in training data increased the performance of the algorithm.
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Kabir Ahmad, Farzana, and Siti Sakira Kamaruddin. "Research Trends in Microarray Data Analysis: Modelling Gene Regulatory Network by Integrating Transcription Factors Data." Scientific Research Journal 12, no. 1 (June 1, 2015): 39. http://dx.doi.org/10.24191/srj.v12i1.5437.
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The invention of microarray technology has enabled expression levels of thousands of genes to be monitored at once. This modernized approach has created large amount of data to be examined. Recently, gene regulatory network has been an interesting topic and generated impressive research goals in computational biology. Better understanding of the genetic regulatory processes would bring significant implications in the biomedical fields and many other pharmaceutical industries. As a result, various mathematical and computational methods have been used to model gene regulatory network from microarray data. Amongst those methods, the Bayesian network model attracts the most attention and has become the prominent technique since it can capture nonlinear and stochastic relationships between variables. However, structure learning of this model is NP-hard and computationally complex as the number of potential edges increase drastically with the number of genes. In addition, most of the studies only focused on the predicted results while neglecting the fact that microarray data is a fragmented information on the whole biological process. Hence, this study proposed a network-based inference model that combined biological knowledge in order to verify the constructed gene regulatory relationships. The gene regulatory network is constructed using Bayesian network based on low-order conditional independence approach. This technique aims to identify from the data the dependencies to construct the network structure, while addressing the structure learning problem. In addition, three main toolkits such as Ensembl, TFSearch and TRANSFAC have been used to determine the false positive edges and verify reliability of regulatory relationships. The experimental results show that by integrating biological knowledge it could enhance the precision results and reduce the number of false positive edges in the trained gene regulatory network.
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Cui, Zhenghang, Nontawat Charoenphakdee, Issei Sato, and Masashi Sugiyama. "Classification from Triplet Comparison Data." Neural Computation 32, no. 3 (March 2020): 659–81. http://dx.doi.org/10.1162/neco_a_01262.
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Learning from triplet comparison data has been extensively studied in the context of metric learning, where we want to learn a distance metric between two instances, and ordinal embedding, where we want to learn an embedding in a Euclidean space of the given instances that preserve the comparison order as much as possible. Unlike fully labeled data, triplet comparison data can be collected in a more accurate and human-friendly way. Although learning from triplet comparison data has been considered in many applications, an important fundamental question of whether we can learn a classifier only from triplet comparison data without all the labels has remained unanswered. In this letter, we give a positive answer to this important question by proposing an unbiased estimator for the classification risk under the empirical risk minimization framework. Since the proposed method is based on the empirical risk minimization framework, it inherently has the advantage that any surrogate loss function and any model, including neural networks, can be easily applied. Furthermore, we theoretically establish an estimation error bound for the proposed empirical risk minimizer. Finally, we provide experimental results to show that our method empirically works well and outperforms various baseline methods.
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Prester, Jasna, and Mihaela Jurić. "Big data for product innovation in manufacturing." Tehnički glasnik 13, no. 1 (March 23, 2019): 36–42. http://dx.doi.org/10.31803/tg-20181011124610.
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The article analyses big data usage in the Croatian manufacturing sector. Big data usage is still low but present. We analysed the influence of six sources of big data and their influence on share of returns generated by new products using two step OLS regression analysis. The results are robust but they show that some sources have positive and some have negative effects on share of returns generated by new products. Based on the most recent research of scholarly papers we define big data and show a clear research gap by linking big data and innovation. That is, only six papers deal with big data and innovation. In five papers big data comes from social media data, and in the remaining one paper they use data from sensors but predominantly to reduce cost or support the product. Therefore, we contribute by closing this research gap of linking big data and innovation.
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Dagogo-Jack, Ibiayi, Geoffrey R. Oxnard, Jessica Fink, Gianluca Diubaldi, Caitlyn Helms, Justin F. Gainor, Michael S. Rabin, et al. "A phase II study of lorlatinib in patients (pts) with ALK-positive (ALK+) lung cancer with brain-only progression." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): 9595. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.9595.
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9595 Background: Lorlatinib is a 3rd-generation ALK tyrosine kinase inhibitor (TKI) developed to penetrate the central nervous system (CNS) and overcome resistance to 2nd-generation (2nd-gen) ALK TKIs. In a phase II study, lorlatinib demonstrated significant intracranial (IC) activity after failure of 2nd-gen TKIs. As treatment discontinuation for extracranial (EC) progression can confound assessment of durability of IC response, we performed a phase II study (NCT02927340) to selectively evaluate lorlatinib activity in ALK+ pts with CNS-only disease. Methods: Between 11/2016 and 1/2019, 22 pts with IC progression on an ALK TKI with no other sites of measurable disease were enrolled at 2 institutions. Pts received lorlatinib at a starting dose of 100 mg QD. The primary endpoint was the IC disease control rate (DCR) at 12 weeks per modified RECIST v1.1. Secondary endpoints were IC objective response rate (ORR), duration of response (DOR), and progression-free survival (PFS). Results: Of the 22 pts enrolled, 21 (95%) had progressed on a 2nd-gen ALK TKI and 14 (64%) had previously received CNS radiation (median 21.1 months between radiation and lorlatinib). Median number of prior ALK TKIs was 2 (range 1-4). As of the data cutoff of 12/15/19, median follow-up was 14 months. At 12 weeks, the IC-DCR was 95%, including 8 pts with stable disease. Best IC ORR was 59% with 6 complete and 7 partial responses. Nine (41%) pts relapsed on study, including 3 IC-only, 5 EC-only, and 1 combined relapse. Four pts continued treatment beyond EC-only progression. Although median IC DOR and PFS were not estimable due to few progression events, the IC progression-free rate at 12 months was 81% (95% CI: 53%-94%). Twelve pts have discontinued study treatment due to progression (n = 6), edema (n = 1), pulmonary hypertension (n = 1), or transition to commercial lorlatinib (n = 4). Conclusions: Lorlatinib induces durable intracranial responses in pts with CNS-only progression on 2nd-gen ALK TKIs, suggesting that CNS-specific relapses are primarily driven by ALK-dependent mechanisms. Further studies are needed to characterize the molecular basis of sensitivity to lorlatinib in this unique subgroup of pts with ALK+ lung cancer. Clinical trial information: NCT02927340 .
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Rose, Ashley, Runa Gokhale, Isaac See, James Baggs, Rachel Slayton, Scott Fridkin, and Kelly Hatfield. "Validation of Administrative Codes for Identification of Staphylococcus aureus Infections Among Electronic Health Data." Infection Control & Hospital Epidemiology 41, S1 (October 2020): s507—s509. http://dx.doi.org/10.1017/ice.2020.1188.
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Background: Epidemiological studies have utilized administrative discharge diagnosis codes to identify methicillin-resistant and methicillin-sensitive Staphylococcus aureus (MRSA and MSSA) infections and trends, despite debate regarding the accuracy of utilizing codes for this purpose. We assessed the sensitivity and positive predictive value (PPV) of MRSA- and MSSA-specific diagnosis codes, trends, characteristics, and outcomes of S. aureus hospitalizations by method of identification. Methods: Clinical micro biology results and discharge data from geographically diverse US hospitals participating in the Premier Healthcare Database from 2012–2017 were used to identify monthly rates of MRSA and MSSA. Positive MRSA or MSSA clinical cultures and/or a MRSA- or MSSA-specific International Classification of Diseases, Ninth/Tenth Revision, Clinical Modification (ICD-9/10 CM) diagnosis codes from adult inpatients (aged ≥18 years) were included as S. aureus hospitalizations. Septicemia was defined as a positive blood culture or a MRSA or MSSA septicemia code. Sensitivity and PPV for codes were calculated for hospitalizations where admission status was not listed as transfer; true infection was considered a positive clinical culture. Negative binominal regression models measured trends in rates of MRSA and MSSA per 1,000 hospital discharges. Results: We identified 168,634 MRSA and 148,776 MSSA hospitalizations in 256 hospitals; 17% of MRSA and 21% of MSSA were septicemia. Less than half of all S. aureus hospitalizations (49% MRSA, 46% MSSA) and S. aureus septicemia hospitalizations (37% MRSA, 38% MSSA) had both a positive culture and diagnosis code (Fig. 1). Sensitivity of MRSA codes in identifying positive cultures was 61% overall and 56% for septicemia, PPV was 62% overall and 53% for septicemia. MSSA codes had a sensitivity of 49% in identifying MSSA cultures and 52% for MSSA septicemia; PPV was 69% overall and 62% for septicemia. Despite low sensitivity, MRSA trends are similar for cultures and codes, and MSSA trends are divergent (Fig. 2). For hospitalizations with septicemia, mortality was highest among those with a blood culture only (31.3%) compared to hospitalizations with both a septicemia code and blood culture (16.6%), and septicemia code only (14.7%). Conclusions: ICD diagnosis code sensitivity and PPV for identifying infections were consistently poor in recent years. Less than half of hospitalizations have concordant microbiology laboratory results and diagnosis codes. Rates and trend estimates for MSSA differ by method of identification. Using diagnosis codes to identify S. aureus infections may not be appropriate for descriptive epidemiology or assessing trends due to significant misclassification.Funding: NoneDisclosures: Scott Fridkin reports that his spouse receives consulting fees from the vaccine industry.
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Hawser, Stephen P., Samuel K. Bouchillon, Daryl J. Hoban, Robert E. Badal, Po-Ren Hsueh, and David L. Paterson. "Emergence of High Levels of Extended-Spectrum-β-Lactamase-Producing Gram-Negative Bacilli in the Asia-Pacific Region: Data from the Study for Monitoring Antimicrobial Resistance Trends (SMART) Program, 2007." Antimicrobial Agents and Chemotherapy 53, no. 8 (June 8, 2009): 3280–84. http://dx.doi.org/10.1128/aac.00426-09.
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ABSTRACT Of 3,004 gram-negative bacilli collected from intra-abdominal infections in the Asia-Pacific region during 2007, 42.2% and 35.8% of Escherichia coli and Klebsiella spp., respectively, were extended-spectrum β-lactamase (ESBL) positive. Moreover ESBL rates in India for E. coli, Klebsiella pneumoniae, and Klebsiella oxytoca were 79.0%, 69.4%, and 100%, respectively. ESBL-positive E. coli rates were also relatively high in China (55.0%) and Thailand (50.8%). Ertapenem and imipenem were the most active drugs tested, inhibiting over 90% of all species, including ESBL-positive isolates with the exception of Pseudomonas aeruginosa isolates (<90% susceptible to all study drugs) and ESBL-positive Klebsiella pneumoniae isolates (<90% susceptible to all study drugs except imipenem). Quinolones achieved 90% inhibition levels only against ESBL-negative K. pneumoniae and ESBL-negative K. oxytoca. A decline in ampicillin-sulbactam activity was noted, with only 34.5% of all Enterobacteriaceae inhibited in this study.
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Rüber, Ina Elisabeth, and Jan Germen Janmaat. "Does Participation in Adult Education Increase Volunteering? An Analysis of British Longitudinal Data." Adult Education Quarterly 71, no. 1 (June 2, 2020): 55–72. http://dx.doi.org/10.1177/0741713620927348.
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High-quality longitudinal data from the UK Household Longitudinal Study gives us the opportunity to investigate whether participation in adult education (AE) fosters volunteering, and whether this depends on the volume of AE, its content, or on the qualification obtained with it. From a public enlightenment perspective, we would only expect to find an effect if the content of AE relates to social issues and domains relevant for volunteering. Yet theories emphasizing AE as a place of encounter and an activity that strengthens self-confidence would expect a positive effect regardless of the content but depending on the volume and the obtained qualification. Our results from a person fixed-effects model reveal a significant and positive effect of participation in AE on volunteering in general, while volume, content, and obtained qualifications appear to make no difference. These findings provisionally suggest that social interactions and self-perceptions explain civic returns to AE.
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Viguier, Jérôme, Yvan Coscas, Chantal Touboul, Jean F. Morere, Jean-Yves Blay, Xavier B. Pivot, Christine Lhomel, and François Eisinger. "Knowledge of the French population on colorectal cancer screening: Data from the EDIFICE 3 survey." Journal of Clinical Oncology 31, no. 4_suppl (February 1, 2013): 352. http://dx.doi.org/10.1200/jco.2013.31.4_suppl.352.
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352 Background: In France, following a pilot population-based screening program in 2002-2003, a national organized program targeting 17 million people was progressively implemented from 2005 to 2009. EDIFICE surveys are iterative polls focusing on cancer screening behavior. The EDIFICE 3 survey was conducted in 2011 and partly dedicated to knowledge of the colorectal cancer screening process. Methods: This third nationwide observational study, EDIFICE 3, was conducted via phone interviews among a representative sample of 946 subjects aged between 50 and 74 years, who had never been treated for cancer. 59% of the population declared having undergone colorectal cancer screening (fecal test or colonoscopy). Results: Interviewed about the screening process, 510/946 (54%) of the French population were unaware of the procedure after a positive fecal test and 782/946 (83%) were unaware of how soon a new test should be performed after a negative result. Only 79/946 (8%) were aware of what to do after either a positive or a negative test and 47% in one out of the two cases. 84% of subjects over assessed (by a factor 2 to 10) the probability of having cancer after a positive test. In contrast, 65% were aware of the possibility of a false negative test. Only 3% of our sample know both the right screening agenda (every other year) and the need for a colonoscopy after a positive test. Neither gender, educational level and socio economic level significantly impacted the rate of right answers. Conclusions: This study demonstrates a lack of detailed knowledge on the colorectal cancer screening process in the French national program. This raises the issue of the fairness of the process (an ethical issue) and may be a reason for the current poor uptake (an effectiveness issue). This should be tackled by improving the transmission of information, preferentially via general practitioners, institutional letters sent directly to subjects (in our survey these two media were found to be readily accepted) and lay press.
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Fan, Yunzhou, Yanyan Wu, Xiongjing Cao, Junning Zou, Ming Zhu, Di Dai, Lin Lu, Xiaoxv Yin, and Lijuan Xiong. "Automated Cluster Detection of Health Care–Associated Infection Based on the Multisource Surveillance of Process Data in the Area Network: Retrospective Study of Algorithm Development and Validation." JMIR Medical Informatics 8, no. 10 (October 23, 2020): e16901. http://dx.doi.org/10.2196/16901.
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Background The cluster detection of health care–associated infections (HAIs) is crucial for identifying HAI outbreaks in the early stages. Objective We aimed to verify whether multisource surveillance based on the process data in an area network can be effective in detecting HAI clusters. Methods We retrospectively analyzed the incidence of HAIs and 3 indicators of process data relative to infection, namely, antibiotic utilization rate in combination, inspection rate of bacterial specimens, and positive rate of bacterial specimens, from 4 independent high-risk units in a tertiary hospital in China. We utilized the Shewhart warning model to detect the peaks of the time-series data. Subsequently, we designed 5 surveillance strategies based on the process data for the HAI cluster detection: (1) antibiotic utilization rate in combination only, (2) inspection rate of bacterial specimens only, (3) positive rate of bacterial specimens only, (4) antibiotic utilization rate in combination + inspection rate of bacterial specimens + positive rate of bacterial specimens in parallel, and (5) antibiotic utilization rate in combination + inspection rate of bacterial specimens + positive rate of bacterial specimens in series. We used the receiver operating characteristic (ROC) curve and Youden index to evaluate the warning performance of these surveillance strategies for the detection of HAI clusters. Results The ROC curves of the 5 surveillance strategies were located above the standard line, and the area under the curve of the ROC was larger in the parallel strategy than in the series strategy and the single-indicator strategies. The optimal Youden indexes were 0.48 (95% CI 0.29-0.67) at a threshold of 1.5 in the antibiotic utilization rate in combination–only strategy, 0.49 (95% CI 0.45-0.53) at a threshold of 0.5 in the inspection rate of bacterial specimens–only strategy, 0.50 (95% CI 0.28-0.71) at a threshold of 1.1 in the positive rate of bacterial specimens–only strategy, 0.63 (95% CI 0.49-0.77) at a threshold of 2.6 in the parallel strategy, and 0.32 (95% CI 0.00-0.65) at a threshold of 0.0 in the series strategy. The warning performance of the parallel strategy was greater than that of the single-indicator strategies when the threshold exceeded 1.5. Conclusions The multisource surveillance of process data in the area network is an effective method for the early detection of HAI clusters. The combination of multisource data and the threshold of the warning model are 2 important factors that influence the performance of the model.
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Tao, Min, Xiaobin Gong, Jian Guan, Junfeng Song, Zhixin Song, Xueyan Li, Shuxu Guo, Jian Chen, Siyao Yu, and Fengli Gao. "Ghost Imaging by a Proportional Parameter to Filter Bucket Data." Applied Sciences 11, no. 1 (December 29, 2020): 227. http://dx.doi.org/10.3390/app11010227.
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Most ghost imaging reconstruction algorithms require a large measurement time to retrieve the object information clearly. But not all groups of data play a positive role in reconstructing the object image. Abandoning some redundant data can not only enhance the quality of reconstruction images but also speed up the computation process. Here, we propose a method to screen the data using two threshold values set by a proportional parameter during the sampling process. Experimental results show that the reserved data after screening can be used in several reconstruction algorithms, and the reconstruction quality is enhanced or at least remains at the same level. Meanwhile, the computing time costs are greatly reduced, and so is the data storage.
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Zancan, M., R. Franceschini, C. Mimmo, M. Vianello, F. Di Tonno, C. Mazzariol, G. Malossini, and M. Gion. "Free DNA in Urine: A New Marker for Bladder Cancer? Preliminary Data." International Journal of Biological Markers 20, no. 2 (April 2005): 134–36. http://dx.doi.org/10.1177/172460080502000209.
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The aim of the present preliminary study was to investigate the presence of free DNA (FDNA) in urine as a possible marker for the diagnosis of bladder cancer. Naturally voided morning urine specimens were collected from 57 patients with suspected bladder cancer before cystoscopy. A standard urine test was performed; the specimens were then processed in order to obtain a quantitative evaluation of the presence of free DNA in the urine. Twenty-two patients were excluded from the study because they had leukocyturia and/or bacteriuria. Free DNA concentrations higher than 250 ng/mL were found in all 16 patients showing bladder cancer at cystoscopy and in seven (36.8%) of the 19 patients with negative cystoscopy. Urinary FDNA seems to have an excellent sensitivity: we observed no false negative cases and 36.8% false positive cases. By contrast, only 6.25% of the bladder cancer patients had positive urine cytology. Our results seem promising, although further studies and larger numbers are needed to define urinary free DNA as a reliable marker of bladder cancer.
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Liu, Hai Yan. "Designed Data Mining System Based on the Distribute Intrusion Detection Designed." Advanced Materials Research 1056 (October 2014): 202–5. http://dx.doi.org/10.4028/www.scientific.net/amr.1056.202.
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With the development of computer and communication technology, the network has become an important part of the global information infrastructure, but the security problem "is constantly exposed to the vast majority of only using firewall this passive defense means within the network, there are still" great security risk which needs some positive active! The defense strategy, the most important one of which is intrusion detection system through the analysis of network or system in a number of key points to collect information and its, find the breach of security strategy behavior and the signs of being attacked, is a combination of software and hardware of intrusion detection.
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Hou, Shiying, and Liangrong Song. "Market Integration and Regional Green Total Factor Productivity: Evidence from China’s Province-Level Data." Sustainability 13, no. 2 (January 6, 2021): 472. http://dx.doi.org/10.3390/su13020472.
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The development of market integration has an important effect on regional green total factor productivity (GTFP). Based on the panel data of 30 provinces in China from 2008 to 2017, this paper studies the spatial effect and transmission mechanism of market integration on regional green total factor productivity by calculating the Malmquist–Luenburger index and using spatial econometric models. It was found that market integration can promote the improvement of regional green total factor productivity. This positive effect is not only directly reflected in the region, it also indirectly promotes the growth of GTFP in nearby regions. In addition, market integration has shown significant positive effects on efficiency improvement and technological progress, and market integration has affected regional green total factor productivity through them. The above conclusions are of great significance for China to develop a green economy and promote high-quality economic transformation.
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Liu, Chanyuan. "Individual investor attention and stock market performance——Based on big data measurement." E3S Web of Conferences 253 (2021): 02009. http://dx.doi.org/10.1051/e3sconf/202125302009.
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Investor attention is a Sizerce resource, and only the information that investors pay attention to can be reflected in the stock market through transactions. Considering about the development of big data, this paper uses data mining and crawling software to crawl the daily indicators of individual investor attention in 2019. Through a fixed-effect panel data regression model, this paper examines the relationship of personal attention and stock market performance indicators over the same period. This paper found that individual investors’ attention can have a significant positive correlation with the liquidity and profitability indicators of stock market performance.
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Zhou, Chen, Mark D. Zelinka, Andrew E. Dessler, and Ping Yang. "An Analysis of the Short-Term Cloud Feedback Using MODIS Data." Journal of Climate 26, no. 13 (July 1, 2013): 4803–15. http://dx.doi.org/10.1175/jcli-d-12-00547.1.
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Abstract The cloud feedback in response to short-term climate variations is estimated from cloud measurements combined with offline radiative transfer calculations. The cloud measurements are made by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite and cover the period 2000–10. Low clouds provide a strong negative cloud feedback, mainly because of their impact in the shortwave (SW) portion of the spectrum. Midlevel clouds provide a positive net cloud feedback that is a combination of a positive SW feedback partially canceled by a negative feedback in the longwave (LW). High clouds have only a small impact on the net cloud feedback because of a close cancellation between large LW and SW cloud feedbacks. Segregating the clouds by optical depth, it is found that the net cloud feedback is set by a positive cloud feedback due to reductions in the thickest clouds (mainly in the SW) and a cancelling negative feedback from increases in clouds with moderate optical depths (also mainly in the SW). The global average SW, LW, and net cloud feedbacks are +0.30 ±1.10, −0.46 ±0.74, and −0.16 ±0.83 W m−2 K−1, respectively. The SW feedback is consistent with previous work; the MODIS LW feedback is lower than previous calculations and there are reasons to suspect it may be biased low. Finally, it is shown that the apparently small control that global mean surface temperature exerts on clouds, which leads to the large uncertainty in the short-term cloud feedback, arises from statistically significant but offsetting relationships between individual cloud types and global mean surface temperature.
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Mishra, Amrita. "A Comprehensive Analysis of Approaches for Sentiment Analysis Using Twitter Data on COVID-19 Vaccines." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 2, no. 2 (June 5, 2021): 1–10. http://dx.doi.org/10.54060/jieee/002.02.009.
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Sentiment Analysis has paved routes for opinion analysis of masses over unrestricted territorial limits. With the advent and growth of social media like Twitter, Facebook, WhatsApp, Snapchat in today’s world, stakeholders and the public often takes to expressing their opinion on them and drawing conclusions. While these social media data are extremely informative and well connected, the major challenge lies in incorporating efficient Text Classification strategies which not only overcomes the unstructured and humongous nature of data but also generates correct polarity of opinions (i.e. positive, negative, and neutral). This paper is a thorough effort to provide a brief study about various approaches to SA including Machine Learning, Lexicon Based, and Automatic Approaches. The paper also highlights the comparison of positive, negative, and neutral tweets of the Sputnik V, Moderna, and Covaxin vaccines used for preventive and emergency use of COVID-19 disease.