Hidden topic–emotion transition model for multi-level social emotion detection

With the fast development of online social platforms, social emotion detection, focusing on predicting readers’ emotions evoked by news articles, has been intensively investigated. Considering emotions as latent variables, various probabilistic graphical models have been proposed for emotion detection. However, the bag-of-words assumption prohibits those models from capturing the inter-relations between sentences in a document. Moreover, existing models can only detect emotions at either the document-level or the sentence-level. In this paper, we propose an effective Bayesian model, called hidden Topic–Emotion Transition model, by assuming that words in the same sentence share the same emotion and topic and modeling the emotions and topics in successive sentences as a Markov chain. By doing so, not only the document-level emotion but also the sentence-level emotion can be detected simultaneously. Experimental results on the two public corpora show that the proposed model outperforms state-of-the-art approaches on both document-level and sentence-level emotion detection

Sequence Dependent Repair of 1,N6-Ethenoadenine by DNA Repair Enzymes ALKBH2, ALKBH3, and AlkB

Mutation patterns of DNA adducts, such as mutational spectra and signatures, are useful tools for diagnostic and prognostic purposes. Mutational spectra of carcinogens derive from three sources: adduct formation, replication bypass, and repair. Here, we consider the repair aspect of 1,N6-ethenoadenine (εA) by the 2-oxoglutarate/Fe(II)-dependent AlkB family enzymes. Specifically, we investigated εA repair across 16 possible sequence contexts (5′/3′ flanking base to εA varied as G/A/T/C). The results revealed that repair efficiency is altered according to sequence, enzyme, and strand context (ss- versus ds-DNA). The methods can be used to study other aspects of mutational spectra or other pathways of repair

Probing the Effect of Bulky Lesion-Induced Replication Fork Conformational Heterogeneity Using 4-Aminobiphenyl-Modified DNA

Bulky organic carcinogens are activated in vivo and subsequently react with nucleobases of cellular DNA to produce adducts. Some of these DNA adducts exist in multiple conformations that are slowly interconverted to one another. Different conformations have been implicated in different mutagenic and repair outcomes. However, studies on the conformation-specific inhibition of replication, which is more relevant to cell survival, are scarce, presumably due to the structural dynamics of DNA lesions at the replication fork. It is difficult to capture the exact nature of replication inhibition by existing end-point assays, which usually detect either the ensemble of consequences of all the conformers or the culmination of all cellular behaviors, such as mutagenicity or survival rate. We previously reported very unusual sequence-dependent conformational heterogeneities involving FABP-modified DNA under different sequence contexts (TG1*G2T [67%B:33%S] and TG1G2*T [100%B], G*, N-(2′-deoxyguanosin-8-yl)-4′-fluoro-4-aminobiphenyl) (Cai et al. Nucleic Acids Research, 46, 6356–6370 (2018)). In the present study, we attempted to correlate the in vitro inhibition of polymerase activity to different conformations from a single FABP-modified DNA lesion. We utilized a combination of surface plasmon resonance (SPR) and HPLC-based steady-state kinetics to reveal the differences in terms of binding affinity and inhibition with polymerase between these two conformers (67%B:33%S and 100%B)

Obtaining SF-6D utilities from FACT-H&N in thyroid carcinoma patients: development and results from a mapping study

ObjectiveThere is limited evidence for mapping clinical tools to preference-based generic tools in the Chinese thyroid cancer patient population. The current study aims to map the FACT-H&N (Functional Assessment of Cancer Therapy-Head and Neck Cancer) to the SF-6D (Short Form Six-Dimension), which will inform future cost-utility analyses related to thyroid cancer treatment.MethodsA total of 1050 participants who completed the FACT-H&N and SF-6D questionnaires were included in the analysis. Four methods of direct and indirect mapping were estimated: OLS regression, Tobit regression, ordered probit regression, and beta mixture regression. We evaluated the predictive performance in terms of root mean square error (RMSE), mean absolute error (MAE), concordance correlation coefficient (CCC), Akaike information criterion (AIC) and Bayesian information criterion (BIC) and the correlation between the observed and predicted SF-6D scores.ResultsThe mean value of SF-6D was 0.690 (SD = 0.128). The RMSE values for the fivefold cross-validation as well as the 30% random sample validation for multiple models in this study were 0.0833-0.0909, MAE values were 0.0676-0.0782, and CCC values were 0.6940-0.7161. SF-6D utility scores were best predicted by a regression model consisting of the total score of each dimension of the FACT-H&N, the square of the total score of each dimension, and covariates including age and gender. We proposed to use direct mapping (OLS regression) and indirect mapping (ordered probit regression) to establish a mapping model of FACT-H&N to SF-6D. The mean SF-6D and cumulative distribution functions simulated from the recommended mapping algorithm generally matched the observed ones.ConclusionsIn the absence of preference-based quality of life tools, obtaining the health status utility of thyroid cancer patients from directly mapped OLS regression and indirectly mapped ordered probit regression is an effective alternative

Adaptive Response Enzyme AlkB Preferentially Repairs 1-Methylguanine and 3-Methylthymine Adducts in Double-Stranded DNA

The AlkB protein is a repair enzyme that uses an α-ketoglutarate/Fe(II)-dependent mechanism to repair alkyl DNA adducts. AlkB has been reported to repair highly susceptible substrates, such as 1-methyladenine and 3-methylcytosine, more efficiently in ss-DNA than in ds-DNA. Here, we tested the repair of weaker AlkB substrates 1-methylguanine and 3-methylthymine and found that AlkB prefers to repair them in ds-DNA. We also discovered that AlkB and its human homologues, ABH2 and ABH3, are able to repair the aforementioned adducts when the adduct is present in a mismatched base pair. These observations demonstrate the strong adaptability of AlkB toward repairing various adducts in different environments. (Chemical Equation Presented)

Pomegranate (Punica granatum) extract and its polyphenols reduce the formation of methylglyoxal-DNA adducts and protect human keratinocytes against methylglyoxal-induced oxidative stress

Pomegranate extract (PE) and its polyphenols have been reported to show skin protective effects but their cytoprotective effects against methylglyoxal (MGO)-induced DNA damage and cell dysfunctions are unclear. Herein, we evaluated whether PE, punicalagin (PA), ellagic acid (EA), and urolithin A (UA), can alleviate MGO-induced DNA damage in human keratinocytes. PE (50 µg/mL) and PA (50 µM) protected DNA integrity and reduced the formation of MGO-DNA adducts and tailed DNA by 60.2 and 49.7%, respectively, in HaCaT cells. PE and PA reduced MGO-induced cytotoxicity by increasing the cell viability (by 17.5 and 15.0%) and decreasing reactive oxygen species (by 28.3 and 30.0%), respectively. PE and PA also ameliorated MGO-induced cell dysfunction by restoring cell adhesion, migration, and wound healing capacity. Findings from this study provide insights into the skin protective effects of PE and its polyphenols supporting their applications as potential bioactive ingredients for cosmeceuticals

Adaptive learning on mobile network traffic data

Machine learning based mobile traffic classification has become a popular topic in recent years. As mobile traffic data is dynamic in nature, the static model has become ineffective for the task of classifying future traffic. This is known as the concept drift problem in data streams. To this end, this paper presents an adaptive mobile traffic classification method. Specifically, a method based on the fuzzy competence model is devised to detect concept drift, and a dynamic learning method is presented to update the classification model, so as to adapt to an ever-changing environment at an appropriate time. The concept drift detection method relies on the data distribution instead of the classification error rate. Furthermore, the weights of flow samples are dynamically updated and flow samples are resampled for training a new model when a concept drift is detected. Moreover, recently trained models are saved and used for classification in weighted voting. The weight of each model is updated according to the performance it obtains on the most recent flow samples. On mobile traffic data, experimental results show that our proposed method obtains lower classification error rate with less time consumption on updating models as compared to related methods designed for handling concept drift problems

A Novel Centralized Range-Free Static Node Localization Algorithm with Memetic Algorithm and Lévy Flight

Node localization, which is formulated as an unconstrained NP-hard optimization problem, is considered as one of the most significant issues of wireless sensor networks (WSNs). Recently, many swarm intelligent algorithms (SIAs) were applied to solve this problem. This study aimed to determine node location with high precision by SIA and presented a new localization algorithm named LMQPDV-hop. In LMQPDV-hop, an improved DV-Hop was employed as an underground mechanism to gather the estimation distance, in which the average hop distance was modified by a defined weight to reduce the distance errors among nodes. Furthermore, an efficient quantum-behaved particle swarm optimization algorithm (QPSO), named LMQPSO, was developed to find the best coordinates of unknown nodes. In LMQPSO, the memetic algorithm (MA) and Lévy flight were introduced into QPSO to enhance the global searching ability and a new fast local search rule was designed to speed up the convergence. Extensive simulations were conducted on different WSN deployment scenarios to evaluate the performance of the new algorithm and the results show that the new algorithm can effectively improve position precision