7 research outputs found
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Bayesian Inference for Assessing Effects of Email Marketing Campaigns
Email marketing has been an increasingly important tool for today’s businesses. In this paper, we propose a counting-process-based Bayesian method for quantifying the effectiveness of email marketing campaigns in conjunction with customer characteristics. Our model explicitly addresses the seasonality of data, accounts for the impact of customer characteristics on their purchasing behavior, and evaluates effects of email offers as well as their interactions with customer characteristics. Using the proposed method, together with a propensity-scorebased unit-matching technique for alleviating potential confounding, we analyze a large email marketing data set of an online ticket marketplace to evaluate the short- and long-term effectiveness of their email campaigns. It is shown that email offers can increase customer purchase rate both immediately and during a longer term. Customers’ characteristics such as length of shopping history, purchase recency, average ticket price, average ticket count, and number of genres purchased also affect customers’ purchase rate. A strong positive interaction is uncovered between email offer and purchase recency, suggesting that customers who have been inactive recently are more likely to take advantage of promotional offers.Statistic
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Exploring genetic associations with ceRNA regulation in the human genome
Abstract Competing endogenous RNAs (ceRNAs) are RNA molecules that sequester shared microRNAs (miRNAs) thereby affecting the expression of other targets of the miRNAs. Whether genetic variants in ceRNA can affect its biological function and disease development is still an open question. Here we identified a large number of genetic variants that are associated with ceRNA's function using Geuvaids RNA-seq data for 462 individuals from the 1000 Genomes Project. We call these loci competing endogenous RNA expression quantitative trait loci or ‘cerQTL’, and found that a large number of them were unexplored in conventional eQTL mapping. We identified many cerQTLs that have undergone recent positive selection in different human populations, and showed that single nucleotide polymorphisms in gene 3΄UTRs at the miRNA seed binding regions can simultaneously regulate gene expression changes in both cis and trans by the ceRNA mechanism. We also discovered that cerQTLs are significantly enriched in traits/diseases associated variants reported from genome-wide association studies in the miRNA binding sites, suggesting that disease susceptibilities could be attributed to ceRNA regulation. Further in vitro functional experiments demonstrated that a cerQTL rs11540855 can regulate ceRNA function. These results provide a comprehensive catalog of functional non-coding regulatory variants that may be responsible for ceRNA crosstalk at the post-transcriptional level
Enhancing SPARQL Query Generation for Knowledge Base Question Answering Systems by Learning to Correct Triplets
Generating SPARQL queries from natural language questions is challenging in Knowledge Base Question Answering (KBQA) systems. The current state-of-the-art models heavily rely on fine-tuning pretrained models such as T5. However, these methods still encounter critical issues such as triple-flip errors (e.g., (subject, relation, object) is predicted as (object, relation, subject)). To address this limitation, we introduce TSET (Triplet Structure Enhanced T5), a model with a novel pretraining stage positioned between the initial T5 pretraining and the fine-tuning for the Text-to-SPARQL task. In this intermediary stage, we introduce a new objective called Triplet Structure Correction (TSC) to train the model on a SPARQL corpus derived from Wikidata. This objective aims to deepen the model’s understanding of the order of triplets. After this specialized pretraining, the model undergoes fine-tuning for SPARQL query generation, augmenting its query-generation capabilities. We also propose a method named “semantic transformation” to fortify the model’s grasp of SPARQL syntax and semantics without compromising the pre-trained weights of T5. Experimental results demonstrate that our proposed TSET outperforms existing methods on three well-established KBQA datasets: LC-QuAD 2.0, QALD-9 plus, and QALD-10, establishing a new state-of-the-art performance (95.0% F1 and 93.1% QM on LC-QuAD 2.0, 75.85% F1 and 61.76% QM on QALD-9 plus, 51.37% F1 and 40.05% QM on QALD-10)
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Spontaneous Internal Electric Field in Heterojunction Boosts Bifunctional Oxygen Electrocatalysts for Zinc-Air Batteries: Theory, Experiment, and Application.
Heterojunctions are a promising class of materials for high-efficiency bifunctional oxygen electrocatalysts in both oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). However, the conventional theories fail to explain why many catalysts behave differently in ORR and OER, despite a reversible path (* O2 ⇋* OOH⇋* O⇋* OH). This study proposes the electron-/hole-rich catalytic center theory (e/h-CCT) to supplement the existing theories, it suggests that the Fermi level of catalysts determines the direction of electron transfer, which affects the direction of the oxidation/reduction reaction, and the density of states (DOS) near the Fermi level determines the accessibility for injecting electrons and holes. Additionally, heterojunctions with different Fermi levels form electron-/hole-rich catalytic centers near the Fermi levels to promote ORR/OER, respectively. To verify the universality of the e/h-CCT theory, this study reveals the randomly synthesized heterostructural Fe3 N-FeN0.0324 (Fex N@PC with DFT calculations and electrochemical tests. The results show that the heterostructural F3 N-FeN0.0324 facilitates the catalytic activities for ORR and OER simultaneously by forming an internal electron-/hole-rich interface. The rechargeable ZABs with Fex N@PC cathode display a high open circuit potential of 1.504 V, high power density of 223.67 mW cm-2 , high specific capacity of 766.20 mAh g-1 at 5 mA cm-2 , and excellent stability for over 300 h
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Predicting regulatory variants with composite statistic
Motivation: Prediction and prioritization of human noncoding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of noncoding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction.
Results: Here, we compiled an integrative resource for predictions from eight different tools on functional annotation of noncoding variants. We further developed a composite strategy to integrate multiple predictions and computed the composite likelihood of a given variant being regulatory variant. Benchmarked by multiple independent causal variants datasets, we demonstrated that our composite model significantly improves the prediction performance.
Availability: We implemented our model and scoring procedure as a tool, named PRVCS, which is freely available to academic and nonprofit usage at http://jjwanglab.org/PRVCS.Statistic