TERMS: textual emotion recognition in multidimensional space.

Microblogs generate a vast amount of data in which users express their emotions regarding almost all aspects of everyday life. Capturing affective content from these context-dependent and subjective texts is a challenging task. We propose an intelligent probabilistic model for textual emotion recognition in multidimensional space (TERMS) that captures the subjective emotional boundaries and contextual information embedded in a text for robust emotion recognition. It is implausible with discrete label assignment;therefore, the model employs a soft assignment by mapping varying emotional perceptions in a multidimensional space and generates them as distributions via the Gaussian mixture model (GMM). To strengthen emotion distributions, TERMS integrates a probabilistic emotion classifier that captures the contextual and linguistic information from texts. The integration of these aspects, the context-aware emotion classifier and the learned GMM parameters provide a complete coverage for accurate emotion recognition. The large-scale experimentation shows that compared to baseline and state-of-the-art models, TERMS achieved better performance in terms of distinguishability, prediction, and classification performance. In addition, TERMS provide insights on emotion classes, the annotation patterns, and the models application in different scenarios.

Effect of Temperature and Pressure on Structural and Optical Properties of Organic-Inorganic Hybrid Manganese Halides.

Organic-inorganic hybrid metal halides have recently attracted attention in the global research field for their bright light emission, tunable photoluminescence wavelength, and convenient synthesis method. This study reports the detailed properties of (C10H16N)2MnBr4, which emits bright green light with a high photoluminescence quantum yield. Results of powder X-ray diffraction, photoluminescence, thermogravimetric analysis, and Raman spectra show the phase transition of (C10H16N)2MnBr4 at 430 K. This phase transition was identified as the solid to liquid state of (C10H16N)2MnBr4. Moreover, the pressure- and temperature-induced relationship between structural and optical properties in (C10H16N)2MnBr4 can be identified. This investigation provides deep insights into the luminescent properties of metal halide crystals and promotes further research.

Functional dyspepsia in depression: A population-based cohort study.

BACKGROUND: Patients with functional dyspepsia (FD) are more likely to have persistent depression, yet whether depression and antidepressant treatments are associated with subsequent risk of FD remain unclear. METHODS: Using population-based insurance administrative data of Taiwan, an 11-year historic cohort study was assembled, comparing cases aged 18 and above with the diagnosis of depressive disorder, to a propensity score-matched sample of adults without depression. Incident FD as a primary diagnosis was ascertained. Hazard ratios of FD were calculated using Cox regression models by age, gender, other comorbidities, nonsteroidal anti-inflammatory medications, antidepressants and antidiabetic agents. RESULTS: A total of 20,197 people with depressive disorder and 20,197 propensity score-matched comparisons without depression were followed up. The incidence of FD was 1.7-fold greater in the depressive cohort than in comparisons (12.9 versus 7.57 per 1000 person-years), with an adjusted hazard ratio (aHR) of 2.16 (95% confidence interval (CI) 1.93~2.41). Increased risks were significant regardless of comorbidities or medication uses, the highest in the untreated depression group compared to the group without depression, with an aHR of 2.51(95% CI 2.15~2.93). CONCLUSIONS: This population-based study showed that patients with depressive disorder are at elevated risk of FD. Antidepressant treatment could reduce the risk of FD.

Joint Selection of Influential Users and Locations under Target Region in Location-Based Social Networks.

Influence Maximization problem, selection of a set of users in a social network to maximize the influence spread, has received ample research attention in the social network analysis domain due to its practical applications. Although the problem has been extensively studied, existing works have neglected the location's popularity and importance along with influential users for product promotion at a particular region in Location-based Social Networks. Real-world marketing companies are more interested in finding suitable locations and influential users in a city to promote their product and attract as many users as possible. In this work, we study the joint selection of influential users and locations within a target region from two complementary perspectives; general and specific location type selection perspectives. The first is to find influential users and locations at a specified region irrespective of location type or category. The second perspective is to recommend locations matching location preference in addition to the target region for product promotion. To address general and specific location recommendations and influential users, we propose heuristic-based methods that effectively find influential users and locations for product promotion. Our experimental results show that it is not always an optimal choice to recommend locations with the highest popularity values, such as ratings, check-ins, and so, which may not be a true indicator of location popularity to be considered for marketing. Our results show that not only influential users are helpful for product promotion, but suitable influential locations can also assist in promoting products in the target region.

Linguistic Patterns for Code Word Resilient Hate Speech Identification.

The permanent transition to online activity has brought with it a surge in hate speech discourse. This has prompted increased calls for automatic detection methods, most of which currently rely on a dictionary of hate speech words, and supervised classification. This approach often falls short when dealing with newer words and phrases produced by online extremist communities. These code words are used with the aim of evading automatic detection by systems. Code words are frequently used and have benign meanings in regular discourse, for instance, "skypes, googles, bing, yahoos" are all examples of words that have a hidden hate speech meaning. Such overlap presents a challenge to the traditional keyword approach of collecting data that is specific to hate speech. In this work, we first introduced a word embedding model that learns the hidden hate speech meaning of words. With this insight on code words, we developed a classifier that leverages linguistic patterns to reduce the impact of individual words. The proposed method was evaluated across three different datasets to test its generalizability. The empirical results show that the linguistic patterns approach outperforms the baselines and enables further analysis on hate speech expressions.

A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles.

In this research, we explored various state-of-the-art biomedical-specific pre-trained Bidirectional Encoder Representations from Transformers (BERT) models for the National Library of Medicine - Chemistry (NLM CHEM) and LitCovid tracks in the BioCreative VII Challenge, and propose a BERT-based ensemble learning approach to integrate the advantages of various models to improve the system's performance. The experimental results of the NLM-CHEM track demonstrate that our method can achieve remarkable performance, with F1-scores of 85% and 91.8% in strict and approximate evaluations, respectively. Moreover, the proposed Medical Subject Headings identifier (MeSH ID) normalization algorithm is effective in entity normalization, which achieved a F1-score of about 80% in both strict and approximate evaluations. For the LitCovid track, the proposed method is also effective in detecting topics in the Coronavirus disease 2019 (COVID-19) literature, which outperformed the compared methods and achieve state-of-the-art performance in the LitCovid corpus. Database URL: https://www.ncbi.nlm.nih.gov/research/coronavirus/.

Relative risk of functional dyspepsia in patients with sleep disturbance: a population-based cohort study.

Increased prevalence of sleep disorders has been found in patients with functional dyspepsia; however, direction of causality remains unclear. Our aim was to compare the risk of incident functional dyspepsia between patients with and without sleep disturbance from a large population-based sample. Utilizing a nation-wide health insurance administrative dataset, we assembled an 11-year historic cohort study to compare subsequent incidence of diagnosed functional dyspepsia between adult patients with any diagnosis of sleep disturbance and age- and gender-matched controls. Hazard ratios adjusted for other relevant comorbidities and medications were calculated using Cox regression models. 45,310 patients with sleep disorder and 90,620 controls were compared. Patients with sleep apnea had a 3.3-fold (95% confidence interval: 2.82 ~ 3.89) increased hazard of functional dyspepsia compared with controls. This increased risk persisted regardless of previously diagnosed depression coexisted. Sleep disturbance was associated with an increased risk of subsequent functional dyspepsia. Potential mechanisms are discussed.

Loss of degradation capacity of activated sludge for a xenobiotic after a period without its influent.

Xenobiotic shock experiments were conducted on lab-scale continuous-flow activated sludge systems to examine activated sludge treatment performance and to determine the xenobiotic degrader loss after periods of xenobiotic absence. The systems were operated with normal influent of a xenobiotic and a biogenic substrate until steady state, and were then artificially disturbed by removing and re-adding the xenobiotic in the influent. Substantial xenobiotic leaks were found when xenobiotic absent time was approximately one mean cell residence time (theta(c)), and the system failed when xenobiotic absent time was longer than a theta(c). Amount of degrader at the time of dual substrate steady state was estimated to be approximately 6% of the total sludge. As the xenobiotic absence time was lengthened, degrader amount in the system was reduced exponentially at a half life of approximately three days. The loss rate could be attributed mainly to the rate of displacement by theta(c) operation, followed by endogenous decay and de-acclimation loss.

Network approach for decision making under risk-How do we choose among probabilistic options with the same expected value?

Conventional decision theory suggests that under risk, people choose option(s) by maximizing the expected utility. However, theories deal ambiguously with different options that have the same expected utility. A network approach is proposed by introducing 'goal' and 'time' factors to reduce the ambiguity in strategies for calculating the time-dependent probability of reaching a goal. As such, a mathematical foundation that explains the irrational behavior of choosing an option with a lower expected utility is revealed, which could imply that humans possess rationality in foresight.

Activated sludge treatment of a xenobiotic with or without a biogenic substrate during start-up and shocks.

Lab-scale continuous flow activated sludge systems that were acclimated to 2,4-dichlorphenoxyacetic acid (2,4-D) under sole 2,4-D influent and without sludge wastage, were able to maintain successful 2,4-D treatment when both 2,4-D and a biogenic substrate were fed and the systems operated with finite mean cell residence times (theta(c)). When the systems were fed dual 2,4-D and biogenic substrates and operated with finite theta(c) from the start, treatment of 2,4-D fluctuated noticeably long after acclimation. At the reintroduction of 2,4-D after its absence from the influent for a period of time (2,4-D shock), the systems under both the sole and dual substrate conditions suffered similar treatment losses; the extent of treatment losses was related to the length of 2,4-D absence time. When shocked, systems with sole 2,4-D influent had a slight advantage over dual substrates by showing a faster recovery from shocks with the help of re-acclimation.

Plasmon-Enhanced Photocurrent using Gold Nanoparticles on a Three-Dimensional TiO2 Nanowire-Web Electrode.

In this study, an anatase/rutile mixed-phase titanium dioxide (TiO2) hierarchical network deposited with Au nanoparticles (Au/TiO2 ARHN) was synthesized using a facile hydrothermal method followed by a simple calcination step. Such a unique structure was designed for improving the light harvest, charge transportation/separation, and the performance of photo-electro-chemical (PEC) cells. The properties of the as-synthesized Au/TiO2 ARHN in PEC cells were investigated by electrochemical measurements using a three-electrode system in a 1 M NaOH electrolyte. Remarkably, a 4.5-folds enhancement of the photocurrent for Au/TiO2 ARHN was observed as compared to that for TiO2 nanowire (NW), under AM1.5G solar illumination, suggesting its potential application in PEC cells. A mechanism has been proposed to explain the high photocurrent of Au/TiO2 ARHN in PEC water splitting.

Effect of spatial constraints on Hardy-Weinberg equilibrium.

Panmixia is a key issue in maintaining genetic diversity, which facilitates evolutionary potential during environmental changes. Additionally, conservation biologists suggest the importance of avoiding small or subdivided populations, which are prone to losing genetic diversity. In this paper, computer simulations were performed to the genetic drift of neutral alleles in random mating populations with or without spatial constraints by randomly choosing a mate among the closest neighbours. The results demonstrated that the number of generations required for the neutral allele to become homozygous (Th) varied proportionally to the population size and also strongly correlated with spatial constraints. The average Th for populations of the same size with spatial constraints was approximately one-and-a-half times longer than without constraints. With spatial constraints, homozygous population clusters formed, which reduced local diversity but preserved global diversity. Therefore, panmixia might be harmful in preserving the genetic diversity of an entire population. The results also suggested that the gene flow or gene exchange among the subdivided populations must be carefully processed to restrict diseases transmission or death during transportation and to monitor the genetic diversity. The application of this concept to similar systems, such as information transfer among peers, is also discussed.

Tools and approaches for the construction of knowledge models from the neuroscientific literature.

Within this paper, we describe a neuroinformatics project (called "NeuroScholar," http://www.neuroscholar.org/) that enables researchers to examine, manage, manipulate, and use the information contained within the published neuroscientific literature. The project is built within a multi-level, multi-component framework constructed with the use of software engineering methods that themselves provide code-building functionality for neuroinformaticians. We describe the different software layers of the system. First, we present a hypothetical usage scenario illustrating how NeuroScholar permits users to address large-scale questions in a way that would otherwise be impossible. We do this by applying NeuroScholar to a "real-world" neuroscience question: How is stress-related information processed in the brain? We then explain how the overall design of NeuroScholar enables the system to work and illustrate different components of the user interface. We then describe the knowledge management strategy we use to store interpretations. Finally, we describe the software engineering framework we have devised (called the "View-Primitive-Data Model framework," [VPDMf]) to provide an open-source, accelerated software development environment for the project. We believe that NeuroScholar will be useful to experimental neuroscientists by helping them interact with the primary neuroscientific literature in a meaningful way, and to neuroinformaticians by providing them with useful, affordable software engineering tools.

Morphological changes of Vibrio parahaemolyticus under cold and starvation stresses.

This study addresses morphological changes of Vibrio parahaemolyticus cells during the initial period of stress. Drastic changes occurred in cells under starvation with/without concomitant cold treatment for one to a few days. Under carbon starvation, rod-shaped cells became coccoid by reductive division, which was influenced by glucose/amino acid in the culture medium. Under concomitant cold and carbon starvation, cells entered the viable but nonculturable state and their shape changed from rod-like to coccoid with waning-shape and various aberrantly intermediates, such as club-shaped, budding, club-shaped with budding, dumbbell-shaped and rabbit-ear-like-shaped. Electron microscopy revealed characteristic features in the cells that were concomitantly stressed with cold and starved, such as densely stained peripheral part and lightly stained central part of cytoplasm, and a thick peptidoglycan cell wall in the temperature-upshifted cells. The expression of cytoskeleton genes, mreB, minE and ftsZ, was determined in a real-time quantitative reverse transcription-polymerase chain reaction, and results revealed a substantial decline in expression in cells under starvation (to 0.25 to 0.64 times that of the control) in the first 2 to 3 h, and a rapid upregulated expression in recovering cells. Alteration of the morphology of cells under such stresses may be associated with the expression of these cytoskeleton genes. This work demonstrates the uniqueness of concomitant cold and starvation treatment in V. parahaemolyticus. The details of the morphological changes may facilitate understanding of stress adaptation in bacteria.

Assessment of gene-covariate interactions by incorporating covariates into association mapping.

The HLA region is considered to be the main genetic risk factor for rheumatoid arthritis. Previous research demonstrated that HLA-DRB1 alleles encoding the shared epitope are specific for disease that is characterized by antibodies to cyclic citrullinated peptides (anti-CCP). In the present study, we incorporated the shared epitope and either anti-CCP antibodies or rheumatoid factor into linkage disequilibrium mapping, to assess the association between the shared epitope or antibodies with the disease gene identified. Incorporating the covariates into the association mapping provides a mechanism 1) to evaluate gene-gene and gene-environment interactions and 2) to dissect the pathways underlying disease induction/progress in quantitative antibodies.

Assessing genotype x environment interaction in linkage mapping using affected sib pairs.

Rheumatoid arthritis (RA) is a complex disease that involves both environmental and genetic factors. Elucidation of the basic etiologic factors involved in RA is essential for preventing and treating this disease. However, the etiology of RA, like that of other complex diseases, is largely unknown. In the present study, we conducted autosomal multipoint linkage scans using affected sib pairs by incorporating the smoking status into analysis. We divided the affected sib pairs into three subgroups based on smoking status (ever, current, or never). Interactions between the susceptibility genes and smoking could then be assessed through linkage mapping. Results suggested that the genetic effect of chromosome 6p21.2-3 in concordant current smoker pairs was about two-fold greater than that of the concordant non-current smoker pairs or discordant pairs. With incorporation of smoking status, additional regions with evidence of linkage were identified, including chromosomes 4q and 20q; while evidence of linkage remained in the regions of chromosomes 6p, 8p, and 9p. The interaction effects varied in different regions. Results from our analyses suggested that incorporating smoking status into linkage analyses could increase the statistical power of the multipoint linkage approach applied here and help elucidate the etiology of RA.

Incorporating quantitative variables into linkage analysis using affected sib pairs.

Rheumatoid arthritis is a complex disease in which environmental factors interact with genetic factors that influence susceptibility. Incorporating information about related quantitative traits or environmental factors into linkage mapping could therefore greatly improve the efficiency and precision of identifying the disease locus. Using a multipoint linkage approach that allows the incorporation of quantitative variables into multipoint linkage mapping based on affected sib pairs, we incorporated data on anti-cyclic citrullinated peptide antibodies, immunoglobulin M rheumatoid factor and age at onset into genome-wide linkage scans. The strongest evidence of linkage was observed on chromosome 6p with a p-value of 3.8 x 10(-15) for the genetic effect. The trait locus is estimated at approximately 45.51-45.82 cM, with standard errors of the estimates range from 0.82 to 1.26 cM, depending on whether and which quantitative variable is incorporated. The standard error of the estimate of trait locus decreased about 28% to 35% after incorporating the additional information from the quantitative variables. This mapping technique helps to narrow down the regions of interest when searching for a susceptibility locus and to elucidate underlying disease mechanisms.

Gene by environment interactions.

This paper summarizes the contributions of group 8 to the Genetic Analysis Workshop 15. Group 8 focused on ways to address the possibility that genetic and environmental effects on phenotype may not be independent, but instead may interact in ways that could play important roles in determining phenotype. Among the eight contributors to this group, all three data sets (expression data, rheumatoid arthritis data, and simulated data) were analyzed. Contributions to this section fell into the two broad categories of refining the data (e.g. stratifying or weighting based on a covariate value) and explicitly modeling the interactions. The contributions also illustrate that there are at least two possible goals for such studies. One goal is simply to identify factors contributing to phenotype in the presence of interactions that might mask the signal to univariate methods. A related but distinct goal is to characterize an interaction (e.g. to determine if the interaction is significant).