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1

Papenbrock, Thorsten, Jens Ehrlich, Jannik Marten, et al. "Functional dependency discovery." Proceedings of the VLDB Endowment 8, no. 10 (2015): 1082–93. http://dx.doi.org/10.14778/2794367.2794377.

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Zhao, Anping, Suresh Manandhar, and Lei Yu. "Topology and semantic based topic dependency structure discovery." Filomat 32, no. 5 (2018): 1843–51. http://dx.doi.org/10.2298/fil1805843z.

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As an important enabler in achieving the maximum potential of text data analysis, topic relationship dependency structure discovery is employed to effectively support the advanced text data analysis intelligent application. The proposed framework combines an analysis approach of complex network and the Latent Dirichlet Allocation (LDA) model for topic relationship network discovery. The approach is to identify topics of the text data based on the LDA and to discover the graphical semantic structure of the intrinsic association dependency between topics. This not only exploits the association dependency between topics but also leverages a series of upper-level semantic topics covered by the text data. The results of evaluation and experimental analysis show that the proposed method is effective and feasible. The results of the proposed work imply that the topics and relationships between them can be detected by this approach. It also provides complete semantic interpretation.
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3

Rajeshwari, T., and C. Thangamani. "Attack Impact Discovery and Recovery with Dynamic Bayesian Networks." Asian Journal of Computer Science and Technology 8, S1 (2019): 74–79. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1953.

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The network attacks are discovered using the Intrusion Detection Systems (IDS). Anomaly, signature and compound attack detection schemes are employed to fetch malicious data traffic activities. The attack impact analysis operations are carried out to discover the malicious objects in the network. The system objects are contaminated with process injection or hijacking. The attack ramification model discovers the contaminated objects. The dependency networks are built to model the information flow over the objects in the network. The dependency network is a directed graph built to indicate the data communication over the objects. The attack ramification models are designed with intrusion root information. The attack ramifications are applied to identify the malicious objects and contaminated objects. The attack ramifications are discovered with the information flows from the attack sources. The Attack Ramification with Bayesian Network (ARBN) scheme discovers the attack impact without the knowledge of the intrusion root. The probabilistic reasoning approach is employed to analyze the object state for ramification process. The objects lifetime is divided into temporal slices to verify the object state changes. The system call traces and object slices are correlated to construct the Temporal Dependency Network (TDN). The Bayesian Network (BN) is constructed with the uncertain data communication activities extracted from the TDN. The attack impact is fetched with loopy belief propagation on the BN model. The network security system is built with attack impact analysis and recovery operations. Live traffic data analysis process is carried out with improved temporal slicing concepts. Attack Ramification and Recovery with Dynamic Bayesian Network (ARRDBN) is built to support attack impact analysis and recovery tasks. The unsupervised attack handling mechanism automatically discovers the feasible solution for the associated attacks.
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Finner, Helmut, Thorsten Dickhaus, and Markus Roters. "Dependency and false discovery rate: Asymptotics." Annals of Statistics 35, no. 4 (2007): 1432–55. http://dx.doi.org/10.1214/009053607000000046.

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Saxena, Hemant, Lukasz Golab, and Ihab F. Ilyas. "Distributed implementations of dependency discovery algorithms." Proceedings of the VLDB Endowment 12, no. 11 (2019): 1624–36. http://dx.doi.org/10.14778/3342263.3342638.

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Ge, Chang, Ihab F. Ilyas, and Florian Kerschbaum. "Secure multi-party functional dependency discovery." Proceedings of the VLDB Endowment 13, no. 2 (2019): 184–96. http://dx.doi.org/10.14778/3364324.3364332.

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7

Caruccio, Loredana, and Loredana Caruccio. "Monitoring Evolution of Dependency Discovery Results." Journal of Visual Language and Computing 2020, no. 2 (2020): 7–16. http://dx.doi.org/10.18293/jvlc2020-n2-007.

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Papenbrock, Thorsten, Sebastian Kruse, Jorge-Arnulfo Quiané-Ruiz, and Felix Naumann. "Divide & conquer-based inclusion dependency discovery." Proceedings of the VLDB Endowment 8, no. 7 (2015): 774–85. http://dx.doi.org/10.14778/2752939.2752946.

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Aldahami, Abdulelah, Yuefeng Li, and Taizan Chan. "Discovery of dependency relations in sequential data flow." Web Intelligence 15, no. 1 (2017): 35–53. http://dx.doi.org/10.3233/web-170351.

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10

Yin, Jianwei, Xinkui Zhao, Yan Tang, Chen Zhi, Zuoning Chen, and Zhaohui Wu. "CloudScout: A Non-Intrusive Approach to Service Dependency Discovery." IEEE Transactions on Parallel and Distributed Systems 28, no. 5 (2017): 1271–84. http://dx.doi.org/10.1109/tpds.2016.2619715.

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11

Zhao, Haibing. "Estimating false discovery proportion in multiple comparison under dependency." Communications in Statistics - Simulation and Computation 46, no. 9 (2016): 6697–704. http://dx.doi.org/10.1080/03610918.2015.1041973.

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DuPont, R. L. "Prevention of adolescent chemical dependency." Journal of Ethnopharmacology 22, no. 3 (1988): 326–27. http://dx.doi.org/10.1016/0378-8741(88)90278-4.

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13

Goethals, Bart, Dominique Laurent, Wim Le Page, and Cheikh Tidiane Dieng. "Mining frequent conjunctive queries in relational databases through dependency discovery." Knowledge and Information Systems 33, no. 3 (2012): 655–84. http://dx.doi.org/10.1007/s10115-012-0526-5.

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Marchi, Fabien De, Stéphane Lopes, and Jean-Marc Petit. "Unary and n-ary inclusion dependency discovery in relational databases." Journal of Intelligent Information Systems 32, no. 1 (2008): 53–73. http://dx.doi.org/10.1007/s10844-007-0048-x.

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15

Piatetsky-Shapiro, Gregory. "Knowledge discovery in databases: Progress report." Knowledge Engineering Review 9, no. 1 (1994): 57–60. http://dx.doi.org/10.1017/s0269888900006573.

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As the number and size of very large databases continues to grow rapidly, so does the need to make sense of them. This need is addressed by the field called knowledge Discovery in Databases (KDD), which combines approaches from machine learning, statistics, intelligent databases, and knowledge acquisition. KDD encompasses a number of different discovery methods, such as clustering, data summarization, learning classification rules, finding dependency networks, analysing changes, and detecting anomalies (Matheus et at., 1993).
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Zhu, Guanghui, Qian Wang, Qiwei Tang, Rong Gu, Chunfeng Yuan, and Yihua Huang. "Efficient and Scalable Functional Dependency Discovery on Distributed Data-Parallel Platforms." IEEE Transactions on Parallel and Distributed Systems 30, no. 12 (2019): 2663–76. http://dx.doi.org/10.1109/tpds.2019.2925014.

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Agarwal, Manoj K., Manish Gupta, Gautam Kar, Anindya Neogi, and Anca Sailer. "Mining activity data for dynamic dependency discovery in e-business systems." IEEE Transactions on Network and Service Management 1, no. 2 (2004): 49–58. http://dx.doi.org/10.1109/tnsm.2004.4798290.

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18

Wang, LiMin. "Extraction of Belief Knowledge from a Relational Database for Quantitative Bayesian Network Inference." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/297121.

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The problem of extracting knowledge from a relational database for probabilistic reasoning is still unsolved. On the basis of a three-phase learning framework, we propose the integration of a Bayesian network (BN) with the functional dependency (FD) discovery technique. Association rule analysis is employed to discover FDs and expert knowledge encoded within a BN; that is, key relationships between attributes are emphasized. Moreover, the BN can be updated by using an expert-driven annotation process wherein redundant nodes and edges are removed. Experimental results show the effectiveness and efficiency of the proposed approach.
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Su, Jie, Jun Li, and Jifeng Chen. "Probabilistic Graph Model Mining User Affinity in Social Networks." International Journal of Web Services Research 18, no. 3 (2021): 22–41. http://dx.doi.org/10.4018/ijwsr.2021070102.

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In social networks, discovery of user similarity is the basis of social media data analysis. It can be applied to user-based product recommendations and inference of user relationship evolution in social networks. In order to effectively describe the complex correlation and uncertainty for social network users, the accuracy of similarity discovery is improved theoretically for massive social network users. Based on the Bayesian network probability map model, network topological structure is combined with the dependency between users, and an effective method is proposed to discover similarity in social network users. To improve the scalability of the proposed method and solve the storage and computation problem of mass data, Bayesian network distributed storage and parallel reasoning algorithm is proposed based on Hadoop platform in this paper. Experimental results verify the efficiency and correctness of the algorithm.
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Guo, Wenge, and M. Bhaskara Rao. "On control of the false discovery rate under no assumption of dependency." Journal of Statistical Planning and Inference 138, no. 10 (2008): 3176–88. http://dx.doi.org/10.1016/j.jspi.2008.01.003.

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21

Strasunskas, Darijus, and Sari E. Hakkarainen. "Domain model-driven software engineering: A method for discovery of dependency links." Information and Software Technology 54, no. 11 (2012): 1239–49. http://dx.doi.org/10.1016/j.infsof.2012.06.004.

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Choueiri, Alexandre Checoli, and Eduardo Alves Portela Santos. "Discovery of path-attribute dependency in manufacturing environments: A process mining approach." Journal of Manufacturing Systems 61 (October 2021): 54–65. http://dx.doi.org/10.1016/j.jmsy.2021.08.005.

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23

Cagliero, Luca, Tania Cerquitelli, and Paolo Garza. "Semi-Automatic Ontology Construction by Exploiting Functional Dependencies and Association Rules." International Journal on Semantic Web and Information Systems 7, no. 2 (2011): 1–22. http://dx.doi.org/10.4018/jswis.2011040101.

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This paper presents a novel semi-automatic approach to construct conceptual ontologies over structured data by exploiting both the schema and content of the input dataset. It effectively combines two well-founded database and data mining techniques, i.e., functional dependency discovery and association rule mining, to support domain experts in the construction of meaningful ontologies, tailored to the analyzed data, by using Description Logic (DL). To this aim, functional dependencies are first discovered to highlight valuable conceptual relationships among attributes of the data schema (i.e., among concepts). The set of discovered correlations effectively support analysts in the assertion of the Tbox ontological statements (i.e., the statements involving shared data conceptualizations and their relationships). Then, the analyst-validated dependencies are exploited to drive the association rule mining process. Association rules represent relevant and hidden correlations among data content and they are used to provide valuable knowledge at the instance level. The pushing of functional dependency constraints into the rule mining process allows analysts to look into and exploit only the most significant data item recurrences in the assertion of the Abox ontological statements (i.e., the statements involving concept instances and their relationships).
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Rother, Marion, Christiane Dimmler, Friderike Weege, Hans-Joachim Mollenkopf, Thomas F. Meyer, and Michael Naumann. "Discovery of Zika virus host dependency factors in trophoblasts using CRISPR/Cas9 screening." Journal of Virological Methods 290 (April 2021): 114085. http://dx.doi.org/10.1016/j.jviromet.2021.114085.

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BROGI, ANTONIO, and SARA CORFINI. "ONTOLOGY- AND BEHAVIOR-AWARE DISCOVERY OF WEB SERVICE COMPOSITIONS." International Journal of Cooperative Information Systems 17, no. 03 (2008): 319–47. http://dx.doi.org/10.1142/s0218843008001853.

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Web service discovery is one of the key issues in the emerging area of Service-oriented Computing. In this paper, we present a complete composition-oriented, ontology-based methodology for discovering semantic Web services, which exploits functional and behavioral properties contained in OWL-S service advertisements to satisfy functional and behavioral client queries. To this aim, we build on top of the results contained in two recent articles, where we presented (1) a suitable data structure (viz. a dependency hypergraph) to collect functional information of services, and (2) a suitable notion of behavioral equivalence for Web services. We also discuss the architecture and the main implementation choices of the matchmaking system applying such a methodology.
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LIN, DEKANG, and PATRICK PANTEL. "Discovery of inference rules for question-answering." Natural Language Engineering 7, no. 4 (2001): 343–60. http://dx.doi.org/10.1017/s1351324901002765.

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One of the main challenges in question-answering is the potential mismatch between the expressions in questions and the expressions in texts. While humans appear to use inference rules such as ‘X writes Y’ implies ‘X is the author of Y’ in answering questions, such rules are generally unavailable to question-answering systems due to the inherent difficulty in constructing them. In this paper, we present an unsupervised algorithm for discovering inference rules from text. Our algorithm is based on an extended version of Harris’ Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus. Essentially, if two paths tend to link the same set of words, we hypothesize that their meanings are similar. We use examples to show that our system discovers many inference rules easily missed by humans.
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Baldwin, Ian. "Discovery of Electricity and the Electromagnetic Force: Its Importance for Environmentalists, Educators, Physicians, Politicians, and Citizens." Advances in Social Sciences Research Journal 7, no. 12 (2020): 362–83. http://dx.doi.org/10.14738/assrj.712.9532.

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The discovery of static electricity in the 18th century and electromagnetism in the 19th was one of the most momentous scientific-technological events in human history. In the 21st century our way of life depends on the electromagnetic force so totally that were our electromagnetic infrastructure to collapse, our civilization would collapse virtually simultaneously. Despite this situation of profound dependency, few citizens understand the electromagnetic force, how it was discovered, how it works, and what wonders of modern life it controls. Nor do citizens understand the roles that Earth’s magnetosphere, ionosphere, and global electric circuit play in making electricity and life possible. Here, I review Earth’s natural electric environment and how electricity first began to be scientifically understood with the innovation of the Leyden jar in the mid-18th century; Franklin’s insights about electricity’s positive and negative poles, and its movement (later named a “current”); Galvani’s discovery of bioelectricity; and Volta’s seminal invention of the bi-metallic electrochemical battery in 1800. Ørsted’s discovery that an electric current affected a magnetized needle, causing it to swivel, in 1820 led to experiments with electromagnets by Schweigger, Arago, Ampère, Sturgeon, Henry, Faraday, and others over the course of the next decade. Observing how conducting wires induced magnetism in iron bars whenever the wires were electrified, Faraday and Henry separately discovered the principle of induction, whereby a moving magnetic field could reciprocally induce electricity in a coiled wire. Out of these momentous discoveries the “magneto-electric” telegraph was invented, and, within a single generation, the world was wired.
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Yamasaki, Seiji, Saya Nagasawa, Mitsuko Hayashi-Nishino, Akihito Yamaguchi, and Kunihiko Nishino. "AcrA dependency of the AcrD efflux pump in Salmonella enterica serovar Typhimurium." Journal of Antibiotics 64, no. 6 (2011): 433–37. http://dx.doi.org/10.1038/ja.2011.28.

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29

Amshakala, K., R. Nedunchezhian, and M. Rajalakshmi. "Extracting Functional Dependencies in Large Datasets Using MapReduce Model." International Journal of Intelligent Information Technologies 10, no. 3 (2014): 19–35. http://dx.doi.org/10.4018/ijiit.2014070102.

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Over the last few years, data are generated in large volume at a faster rate and there has been a remarkable growth in the need for large scale data processing systems. As data grows larger in size, data quality is compromised. Functional dependencies representing semantic constraints in data are important for data quality assessment. Executing functional dependency discovery algorithms on a single computer is hard and laborious with large data sets. MapReduce provides an enabling technology for large scale data processing. The open-source Hadoop implementation of MapReduce has provided researchers a powerful tool for tackling large-data problems in a distributed manner. The objective of this study is to extract functional dependencies between attributes from large datasets using MapReduce programming model. Attribute entropy is used to measure the inter attribute correlations, and exploited to discover functional dependencies hidden in the data.
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Tsai, Kun-Che, Shin-Yu Fang, Shu-Jyuan Yang, Ming-Jium Shieh, Win-Li Lin, and Wen-Shiang Chen. "Time dependency of ultrasound-facilitated gene transfection." Journal of Gene Medicine 11, no. 8 (2009): 729–36. http://dx.doi.org/10.1002/jgm.1347.

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Shenouda, John, and Matthew Clayton. "When patients’ priorities conflict with those of their medical team; a challenging case of a bleeding patient and his dying pet." BMJ Case Reports 14, no. 1 (2021): e237942. http://dx.doi.org/10.1136/bcr-2020-237942.

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We present an unusual case of an acutely unwell patient with an upper gastrointestinal bleed whose resuscitation efforts were delayed by the discovery of his, similarly, acutely unwell pet on the medical high dependency unit. We highlight the challenges this provided the clinical team and focus on the issues relating to patient safety, consent and multidisciplinary action which may be more relevant to daily clinical practice.
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Verrier, Eloi R., Amélie Weiss, Charlotte Bach, et al. "Combined small molecule and loss-of-function screen uncovers estrogen receptor alpha and CAD as host factors for HDV infection and antiviral targets." Gut 69, no. 1 (2019): 158–67. http://dx.doi.org/10.1136/gutjnl-2018-317065.

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ObjectiveHepatitis D virus (HDV) is a circular RNA virus coinfecting hepatocytes with hepatitis B virus. Chronic hepatitis D results in severe liver disease and an increased risk of liver cancer. Efficient therapeutic approaches against HDV are absent.DesignHere, we combined an RNAi loss-of-function and small molecule screen to uncover host-dependency factors for HDV infection.ResultsFunctional screening unravelled the hypoxia-inducible factor (HIF)-signalling and insulin-resistance pathways, RNA polymerase II, glycosaminoglycan biosynthesis and the pyrimidine metabolism as virus-hepatocyte dependency networks. Validation studies in primary human hepatocytes identified the carbamoyl-phosphatesynthetase 2, aspartate transcarbamylase and dihydroorotase (CAD) enzyme and estrogen receptor alpha (encoded by ESR1) as key host factors for HDV life cycle. Mechanistic studies revealed that the two host factors are required for viral replication. Inhibition studies using N-(phosphonoacetyl)-L-aspartic acid and fulvestrant, specific CAD and ESR1 inhibitors, respectively, uncovered their impact as antiviral targets.ConclusionThe discovery of HDV host-dependency factors elucidates the pathogenesis of viral disease biology and opens therapeutic strategies for HDV cure.
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Willard, Francis S., Ana B. Bueno, and Kyle W. Sloop. "Small Molecule Drug Discovery at the Glucagon-Like Peptide-1 Receptor." Experimental Diabetes Research 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/709893.

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The therapeutic success of peptide glucagon-like peptide-1 (GLP-1) receptor agonists for the treatment of type 2 diabetes mellitus has inspired discovery efforts aimed at developing orally available small molecule GLP-1 receptor agonists. Although the GLP-1 receptor is a member of the structurally complex class B1 family of GPCRs, in recent years, a diverse array of orthosteric and allosteric nonpeptide ligands has been reported. These compounds include antagonists, agonists, and positive allosteric modulators with intrinsic efficacy. In this paper, a comprehensive review of currently disclosed small molecule GLP-1 receptor ligands is presented. In addition, examples of “ligand bias” and “probe dependency” for the GLP-1 receptor are discussed; these emerging concepts may influence further optimization of known molecules or persuade designs of expanded screening strategies to identify novel chemical starting points for GLP-1 receptor drug discovery.
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Ligat, Gaëtan, Kaku Goto, Eloi Verrier, and Thomas F. Baumert. "Targeting Viral cccDNA for Cure of Chronic Hepatitis B." Current Hepatology Reports 19, no. 3 (2020): 235–44. http://dx.doi.org/10.1007/s11901-020-00534-w.

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Abstract Purpose of Review Chronic hepatitis B (CHB), caused by hepatitis B virus (HBV), is a major cause of advanced liver disease and hepatocellular carcinoma (HCC) worldwide. HBV replication is characterized by the synthesis of covalently closed circular (ccc) DNA which is not targeted by antiviral nucleos(t)ide analogues (NUCs) the key modality of standard of care. While HBV replication is successfully suppressed in treated patients, they remain at risk for developing HCC. While functional cure, characterized by loss of HBsAg, is the first goal of novel antiviral therapies, curative treatments eliminating cccDNA remain the ultimate goal. This review summarizes recent advances in the discovery and development of novel therapeutic strategies and their impact on cccDNA biology. Recent Findings Within the last decade, substantial progress has been made in the understanding of cccDNA biology including the discovery of host dependency factors, epigenetic regulation of cccDNA transcription and immune-mediated degradation. Several approaches targeting cccDNA either in a direct or indirect manner are currently at the stage of discovery, preclinical or early clinical development. Examples include genome-editing approaches, strategies targeting host dependency factors or epigenetic gene regulation, nucleocapsid modulators and immune-mediated degradation. Summary While direct-targeting cccDNA strategies are still largely at the preclinical stage of development, capsid assembly modulators and immune-based approaches have reached the clinical phase. Clinical trials are ongoing to assess their efficacy and safety in patients including their impact on viral cccDNA. Combination therapies provide additional opportunities to overcome current limitations of individual approaches.
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Zoghbi, Jean-Paul A. "Quantization of Planetary Systems and its Dependency on Stellar Rotation." Publications of the Astronomical Society of Australia 28, no. 3 (2011): 177–201. http://dx.doi.org/10.1071/as09062.

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AbstractWith the discovery of now more than 500 exoplanets, we present a statistical analysis of the planetary orbital periods and their relationship to the rotation periods of their parent stars. We test whether the structural variables of planetary orbits, i.e. planetary angular momentum and orbital period, are ‘quantized’ in integer or half-integer multiples of the parent star's rotation period. The Solar System is first shown to exhibit quantized planetary orbits that correlate with the Sun's rotation period. The analysis is then expanded over 443 exoplanets to statistically validate this quantization and its association with stellar rotation. The results imply that the exoplanetary orbital periods are highly correlated with the parent star's rotation periods and follow a discrete half-integer relationship with orbital ranks n = 0.5, 1.0, 1.5, 2.0, 2.5, etc. The probability of obtaining these results by pure chance is p < 0.024. We discuss various mechanisms that could justify this planetary quantization, such as the hybrid gravitational instability models of planet formation, along with possible physical mechanisms such as the inner disc's magnetospheric truncation, tidal dissipation, and resonance trapping. In conclusion, we statistically demonstrate that a quantized orbital structure should emerge from the formation processes of planetary systems and that this orbital quantization is highly dependent on the parent star's rotation period.
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Rago, Florencia, GiNell Elliott, Ailing Li, et al. "The Discovery of SWI/SNF Chromatin Remodeling Activity as a Novel and Targetable Dependency in Uveal Melanoma." Molecular Cancer Therapeutics 19, no. 10 (2020): 2186–95. http://dx.doi.org/10.1158/1535-7163.mct-19-1013.

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Goudie, AJ. "Comparative effects of cathinone and amphetamine on fixed-interval opérant responding: A rate-dependency analysis." Journal of Ethnopharmacology 17, no. 3 (1986): 298–99. http://dx.doi.org/10.1016/0378-8741(86)90124-8.

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Zhang, Qingyang. "Testing Differential Gene Networks under Nonparanormal Graphical Models with False Discovery Rate Control." Genes 11, no. 2 (2020): 167. http://dx.doi.org/10.3390/genes11020167.

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The nonparanormal graphical model has emerged as an important tool for modeling dependency structure between variables because it is flexible to non-Gaussian data while maintaining the good interpretability and computational convenience of Gaussian graphical models. In this paper, we consider the problem of detecting differential substructure between two nonparanormal graphical models with false discovery rate control. We construct a new statistic based on a truncated estimator of the unknown transformation functions, together with a bias-corrected sample covariance. Furthermore, we show that the new test statistic converges to the same distribution as its oracle counterpart does. Both synthetic data and real cancer genomic data are used to illustrate the promise of the new method. Our proposed testing framework is simple and scalable, facilitating its applications to large-scale data. The computational pipeline has been implemented in the R package DNetFinder, which is freely available through the Comprehensive R Archive Network.
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Tempelmeier, Nicolas, Udo Feuerhake, Oskar Wage, and Elena Demidova. "Mining Topological Dependencies of Recurrent Congestion in Road Networks." ISPRS International Journal of Geo-Information 10, no. 4 (2021): 248. http://dx.doi.org/10.3390/ijgi10040248.

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The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and the scheduling of public transportation services. While most existing studies investigate temporal patterns of RC phenomena, the influence of the road network topology on RC is often overlooked. This article proposes the ST-Discovery algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates effective data-driven discovery of RC dependencies induced by the road network topology using real-world traffic data. We factor out regularly reoccurring traffic phenomena, such as rush hours, mainly induced by the daytime, by modelling and systematically exploiting temporal traffic load outliers. We present an algorithm that first constructs connected subgraphs of the road network based on the traffic speed outliers. Second, the algorithm identifies pairs of subgraphs that indicate spatio-temporal correlations in their traffic load behaviour to identify topological dependencies within the road network. Finally, we rank the identified subgraph pairs based on the dependency score determined by our algorithm. Our experimental results demonstrate that ST-Discovery can effectively reveal topological dependencies in urban road networks.
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Cui, Ze-Jia, Min Gao, Yuan Quan, et al. "Systems Pharmacology-Based Precision Therapy and Drug Combination Discovery for Breast Cancer." Cancers 13, no. 14 (2021): 3586. http://dx.doi.org/10.3390/cancers13143586.

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Breast cancer (BC) is a common disease and one of the main causes of death in females worldwide. In the omics era, researchers have used various high-throughput sequencing technologies to accumulate massive amounts of biomedical data and reveal an increasing number of disease-related mutations/genes. It is a major challenge to use these data effectively to find drugs that may protect human health. In this study, we combined the GeneRank algorithm and gene dependency network to propose a precision drug discovery strategy that can recommend drugs for individuals and screen existing drugs that could be used to treat different BC subtypes. We used this strategy to screen four BC subtype-specific drug combinations and verified the potential activity of combining gefitinib and irinotecan in triple-negative breast cancer (TNBC) through in vivo and in vitro experiments. The results of cell and animal experiments demonstrated that the combination of gefitinib and irinotecan can significantly inhibit the growth of TNBC tumour cells. The results also demonstrated that this systems pharmacology-based precision drug discovery strategy effectively identified important disease-related genes in individuals and special groups, which supports its efficiency, high reliability, and practical application value in drug discovery.
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41

Stoljar, Daniel. "Williamson on Laws and Progress in Philosophy." Epistemology & Philosophy of Science 56, no. 2 (2019): 37–42. http://dx.doi.org/10.5840/eps201956226.

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Williamson rejects the stereotype that there is progress in science but none in philosophy on the grounds (a) that it assumes that in science progress consists in the discovery of universal laws and (b) that this assumption is false, since in both science and philosophy progress consists at least sometimes in the development of better models. I argue that the assumption is false for a more general reason as well: that progress in both science and philosophy consists in the provision of better information about dependency structures.
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42

Taylor, Laura, and William G. Brandt. "Assessing Economic Damages in Personal Injury and Wrongful Death Litigation: The State of Washington." Journal of Forensic Economics 26, no. 1 (2015): 115–31. http://dx.doi.org/10.5085/foen-26-01-09.1.

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Abstract In contribution to the series on economic damages in personal injury and wrongful death cases by state, this article presents the legal framework for calculating economic damages in personal injury and wrongful death actions in the State of Washington. Topics covered include the Washington State court system, expert testimony and discovery, survival and wrongful death statutes, dependency, life expectancy, earning capacity, household services, taxes, consumption and present value. Also briefly discussed are investment income, lost inheritance, post-injury death, mitigation, collateral sources, and punitive damages.
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43

Zhou, Yujia, Gregory P. Takacs, Jatinder K. Lamba, Christopher Vulpe, and Christopher R. Cogle. "Functional Dependency Analysis Identifies Potential Druggable Targets in Acute Myeloid Leukemia." Cancers 12, no. 12 (2020): 3710. http://dx.doi.org/10.3390/cancers12123710.

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Refractory disease is a major challenge in treating patients with acute myeloid leukemia (AML). Whereas the armamentarium has expanded in the past few years for treating AML, long-term survival outcomes have yet to be proven. To further expand the arsenal for treating AML, we searched for druggable gene targets in AML by analyzing screening data from a lentiviral-based genome-wide pooled CRISPR-Cas9 library and gene knockout (KO) dependency scores in 15 AML cell lines (HEL, MV411, OCIAML2, THP1, NOMO1, EOL1, KASUMI1, NB4, OCIAML3, MOLM13, TF1, U937, F36P, AML193, P31FUJ). Ninety-four gene KOs met the criteria of (A) specifically essential to AML cell survival, (B) non-essential in non-AML cells, and (C) druggable according to three-dimensional (3D) modeling or ligand-based druggability scoring. Forty-four of 94 gene-KOs (47%) had an already-approved drug match and comprised a drug development list termed “deKO.” Fifty of 94 gene-KOs (53%) had no drug in development and comprised a drug discovery list termed “disKO.” STRING analysis and gene ontology categorization of the disKO targets preferentially cluster in the metabolic processes of UMP biosynthesis, IMP biosynthesis, dihydrofolate metabolism, pyrimidine nucleobase biosynthesis, vitellogenesis, and regulation of T cell differentiation and hematopoiesis. Results from this study serve as a testable compendium of AML drug targets that, after validation, may be translated into new therapeutics.
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44

Rahmadi, Mahardian, Dian Suasana, Silvy Restuning Lailis, Dinda Monika Nusantara Ratri, and Chrismawan Ardianto. "The effects of quercetin on nicotine-induced reward effects in mice." Journal of Basic and Clinical Physiology and Pharmacology 32, no. 4 (2021): 327–33. http://dx.doi.org/10.1515/jbcpp-2020-0418.

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Abstract Objectives Tobacco smoking remains the primary cause of preventable mortality and morbidity in the world. The complexity of the nicotine dependency process included the withdrawal effect that triggers recurrence being the main problem. Quercetin, known as an antioxidant, binds free radicals and modulates endogenous antioxidants through Nrf2 activations is expected as a potential agent to reduce the risk of nicotine dependence. This research aims to evaluate quercetin’s effects on reducing the risk of nicotine addiction. Methods Conditioned Place Preference (CPP) with a biased design was used to evaluate nicotine’s reward effects in male Balb/C mice. Preconditioning test was performed on day 1; conditioning test was done twice daily on day 2–4 by administering quercetin (i.p.) 50 mg/kg along with nicotine (s.c.) 0.5 mg/kg or Cigarette Smoke Extract (CSE) (s.c.) contained nicotine 0.5 mg/kg; and postconditioning test was performed on day 5 continue with extinction test on day 6, 8, 10, 12, and reinstatement test on day 13. The duration spent in each compartment was recorded and analyzed. Results Nicotine 0.5 mg/kg and CSE 0.5 mg/kg significantly induced reward effects (p<0.05). There was no decrease of reward effect during the extinction-reinstatement stage of the postconditioning phase (p>0.05), while quercetin 50 mg/kg both induced along with nicotine or CSE was able to inhibit the reward effect of nicotine (p>0.05). Conclusions Quercetin reduced the risk of nicotine dependence and has a potential effect to use as a therapy for nicotine dependence, especially as a preventive agent.
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45

Kaisman-Elbaz, T., Y. Elbaz, V. Merkin, et al. "P14.21 Tehila Kaisman-Elbaz MD/PhD." Neuro-Oncology 21, Supplement_3 (2019): iii71. http://dx.doi.org/10.1093/neuonc/noz126.256.

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Abstract BACKGROUND Glioblastoma is known for its dismal prognosis though its dependency on patients’ readily available RBCs parameters defining the patient’s anemic status such as hemoglobin level and Red blood cells distribution Width (RDW) is not fully established. Several works demonstrated a connection between low hemoglobin level or high RDW values to overall glioblastoma patient’s survival, but in other works, a clear connection was not found. This study addresses this unclarity. MATERIAL AND METHODS In this work, 170 glioblastoma patients, diagnosed and treated in Soroka University Medical Center (SUMC) in the last 12 years were retrospectively inspected for their survival dependency on pre-operative RBCs parameters using multivariate analysis followed by false discovery rate procedure due to the multiple hypothesis testing. A survival stratification tree and Kaplan-Meier survival curves that indicate the patient’s prognosis according to these parameters were prepared. RESULTS Beside KPS>70 and tumor resection supplemented by oncological treatment, age<70 (HR=0.4, 95% CI 0.24–0.65), low hemoglobin level (HR=1.79, 95% CI 1.06–2.99) and RDW<14% (HR=0.57, 95% CI 0.37–0.88) were found to be prognostic to patients’ overall survival in multivariate analysis, accounting for false discovery rate of less than 5%. CONCLUSION A survival stratification highlighted a non-anemic subgroup of nearly 30% of the cohort’s patients whose median overall survival was 21.1 months (95% CI 16.2–27.2) - higher than the average Stupp protocol overall median survival of about 15 months. A discussion on the beneficial or detrimental effect of RBCs parameters on glioblastoma prognosis and its possible causes is given.
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46

Lee, Jungwhoi, Jungsul Lee, Woogwang Sim, and Jae-Hoon Kim. "Differential Dependency of Human Pancreatic Cancer Cells on Targeting PTEN via PLK 1 Expression." Cancers 12, no. 2 (2020): 277. http://dx.doi.org/10.3390/cancers12020277.

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Even though the tumour suppressive role of PTEN is well-known, its prognostic implications are ambiguous. The objective of this study was to further explore the function of PTEN expression in human pancreatic cancer. The expression of PTEN has been dominant in various human cancers including pancreatic cancer when compared with their matched normal tissues. The pancreatic cancer cells have been divided into PTEN blockade-susceptible and PTEN blockade-impassible groups dependent on targeting PTEN by altering intracellular signaling. The expression of PTEN has led to varying clinical outcomes of pancreatic cancer based on GEO Series (GSE) data analysis and Liptak’s z analysis. Differential dependency to PTEN blockade has been ascertained based on the expression of polo-like kinase1 PLK1 in pancreatic cancer cells. The prognostic value of PTEN also depends on PLK1 expression in pancreatic cancer. Collectively, the present study provides a rationale for targeting PTEN as a promising therapeutic strategy dependent on PLK1 expressions using a companion biomarker discovery platform.
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47

To, Emma Miriam Yin-Hang, and Rodrigo Acuña. "China and Venezuela: South-South Cooperation or Rearticulated Dependency?" Latin American Perspectives 46, no. 2 (2018): 126–40. http://dx.doi.org/10.1177/0094582x18813574.

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China’s increased participation in the world market and its consequent demand for energy has contributed to exacerbating the vulnerability of many externally oriented resource-rich countries. As a consequence, since the early 2000s the relationship between China and Venezuela has gone far beyond trade to an “energy cooperation” model with joint ventures and development funds not only in oil but also in nonresource sectors of the Venezuelan economy. Despite massive increases in social expenditure, Latin American theorists arguing from a neo-dependency perspective have questioned the long-term benefits of this so-called cooperation. They have characterized the relationship as “neoextractivist” in reference to the historically dependent relationship between Latin America and countries in the Global North. Whether the relationship is seen as based on South-South cooperation or on rearticulated global political and economic inequalities depends on whether the focus is on the behavior of the Chinese state and Chinese companies or on bilateral agreements between the two states. Although politically the relationship is based on cooperation, economically it displays many of the structural problems that Venezuela has faced since the discovery of oil. La mayor participación de China en el mercado mundial y la consiguiente demanda de energía han contribuido a exacerbar la vulnerabilidad de muchos países ricos en recursos con orientación externa. Como consecuencia, desde principios de la década de 2000, la relación entre China y Venezuela ha ido mucho más allá del comercio a un modelo de “cooperación energética” con empresas conjuntas y fondos de desarrollo no solo en el sector petrolero sino también en los sectores de la economía venezolana fuera de los recursos. A pesar de los aumentos masivos en el gasto social, los teóricos latinoamericanos que argumentan desde una perspectiva neo-dependentista han cuestionado los beneficios a largo plazo de esta llamada cooperación. Han caracterizado la relación como "neoextractivista" en referencia a la relación históricamente dependiente entre América Latina y los países del Norte Global. Si se considera que la relación se basa en la cooperación Sur-Sur o en desigualdades políticas y económicas globales rearticuladas depende de si la atención se centra en el comportamiento del estado chino y las empresas chinas o en los acuerdos bilaterales entre los dos estados. Aunque políticamente la relación se basa en la cooperación, económicamente muestra muchos de los problemas estructurales que Venezuela ha enfrentado desde el descubrimiento del petróleo.
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48

Dravid, Vinayak P., X. Lin, V. Ravikumar, R. Rodrigues, and N. Wilcox. "Transmission electron spectroscopy and interferometry of electroceramic oxides." Proceedings, annual meeting, Electron Microscopy Society of America 52 (1994): 542–43. http://dx.doi.org/10.1017/s042482010017044x.

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Research on ferroelectricity and related phenomena has come a long way since the discovery of hysteretic nonlinearity of polarization in "Rochelle Salt" by J. Valasek in 1920's. Ever since the discovery, microstructural issues have dominated the field. With the current trends in using ferroelectric and related oxides in nanocrystalline and thin film forms, techniques for microstructural analysis for such specimens are becoming more sophisticated.We are currently investigating nanocrystalline TiO2 as a model system to examine size dependency of electronic structure in nanocrystals, as reflected in their energy loss spectra. Electron beam evaporated nanocrystals of TiO2 (size ranging from 2 - 10 nm) have been examined with transmission EELS with a ~ 0.8 nm electron probe of our HF-2000 FE TEM. The particles were dispersed on a holey carbon grid and those protruding into the holes were analyzed for changes in valence and core loss spectra as a function of nominal particle size. The plasmon loss energies were observed to increase with decrease in particle size.
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Ņikiforova, Oksana, Vitaly Zabiniako, Jurijs Kornienko, Madara Gasparoviča-Asīte, and Amanda Siliņa. "Mapping of Source and Target Data for Application to Machine Learning Driven Discovery of IS Usability Problems." Applied Computer Systems 26, no. 1 (2021): 22–30. http://dx.doi.org/10.2478/acss-2021-0003.

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Abstract Improving IS (Information System) end-user experience is one of the most important tasks in the analysis of end-users behaviour, evaluation and identification of its improvement potential. However, the application of Machine Learning methods for the UX (User Experience) usability and effic iency improvement is not widely researched. In the context of the usability analysis, the information about behaviour of end-users could be used as an input, while in the output data the focus should be made on non-trivial or difficult attention-grabbing events and scenarios. The goal of this paper is to identify which data potentially can serve as an input for Machine Learning methods (and accordingly graph theory, transformation methods, etc.), to define dependency between these data and desired output, which can help to apply Machine Learning / graph algorithms to user activity records.
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50

Sattari, Saeed, and Fakhreddin Jamali. "Evidence of absorption rate dependency of ibuprofen inversion in the rat." Chirality 6, no. 5 (1994): 435–39. http://dx.doi.org/10.1002/chir.530060512.

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