Soft congruence relations over rings.
Ontology highlight
ABSTRACT:
SUBMITTER: Xin X
PROVIDER: S-EPMC4032665 | biostudies-other | 2014
REPOSITORIES: biostudies-other
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Project description:Let C0(G) denote the near-ring of congruence preserving functions of the group G. We investigate the question "When is C0(G) a ring?". We obtain information externally via the lattice structure of the normal subgroups of G and internally via structural properties of the group G.
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Project description:A new kind of fuzzy module over a fuzzy ring is introduced by generalizing Yuan and Lee's definition of the fuzzy group and Aktaş and Çağman's definition of fuzzy ring. The concepts of fuzzy submodule, and fuzzy module homomorphism are studied and some of their basic properties are presented analogous of ordinary module theory.
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Project description:BACKGROUND:Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach. OBJECTIVE:To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs. METHODS:We used 7000 treats and 2918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80-20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios. RESULTS:Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset. CONCLUSIONS:We employed semantic graph patterns connecting pairs of candidate biomedical entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction.
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Project description:Background and aimPublic health policy development is subject to a range of stakeholders presenting their arguments to influence opinion on the best options for policy action. This paper compares stakeholders' positions in the discourse networks of two pricing policy debates in the United Kingdom: minimum unit pricing for alcohol (MUP) and the soft drinks industry levy (SDIL).DesignDiscourse analysis was combined with network visualization to create representations of stakeholders' positions across the two policy debates as they were represented in 11 national UK newspapers.SettingUnited Kingdom.ObservationsFor the MUP debate 1924 statements by 152 people from 87 organizations were coded from 348 articles. For the SDIL debate 3883 statements by 214 people from 175 organizations were coded from 511 articles.MeasurementsNetwork analysis techniques were used to identify robust argumentative similarities and maximize the identification of network structures. Network measures of size, connectedness and cohesion were used to compare discourse networks.FindingsThe networks for both pricing debates involve a similar range of stakeholder types and form clusters representing policy discourse coalitions. The SDIL network is larger than the MUP network, particularly the proponents' cluster, with more than three times as many stakeholders. Both networks have tight clusters of manufacturers, think-tanks and commercial analysts in the opponents' coalition. Public health stakeholders appear in both networks, but no health charity or advocacy group is common to both.ConclusionA comparison of the discourse in the UK press during the policy development processes for minimum unit pricing for alcohol and the soft drinks industry levy suggests greater cross-sector collaboration among policy opponents than proponents.
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Project description:BackgroundComprehensive conjoint characterization of long-term trajectories representing several biological systems is lacking.MethodsWe measured serially indicators representing 14 distinct biological systems in up to 3,453 participants attending four Framingham Study examinations: bone mineral density, body mass index (BMI), C-reactive protein, glomerular filtration rate, forced vital capacity (FVC), 1 second forced expiratory volume/FVC ratio (FEV1/FVC), gait speed, grip strength, glycosylated hemoglobin (HbA1c), heart rate, left ventricular mass, Mini-Mental State Examination (MMSE), pulse pressure, and total/high-density lipoprotein cholesterol ratio (TC/HDL).ResultsWe observed that correlations among the 14 sex-specific trajectories were modest (r < .30 for 169 of 182 sex-specific correlations). During follow-up (median 8 years), 232 individuals experienced a cardiovascular disease (CVD) event and 393 participants died. In multivariable regression models, CVD incidence was positively related to trajectories of BMI, HbA1c, TC/HDL, gait time, and pulse pressure (p < .06); mortality risk was related directly to trajectories of gait time, C-reactive protein, heart rate, and pulse pressure but inversely to MMSE and FEV1/FVC (p < .006). A unit increase in the trajectory risk score was associated with a 2.80-fold risk of CVD (95% confidence interval [CI], 2.04-3.84; p < .001) and a 2.71-fold risk of death (95% CI, 2.30-3.20; p < .001). Trajectory risk scores were suggestive of a greater increase in model c-statistic compared with single occasion measures (delta-c compared with age- and sex-adjusted models: .032 vs .026 for CVD; .042 vs .030 for mortality).ConclusionsBiological systems age differentially over the life course. Longitudinal data on a parsimonious set of biomarkers reflecting key biological systems may facilitate identification of high-risk individuals.
| S-EPMC7357457 | biostudies-literature
Project description:BackgroundThe retention of ancestral juvenile characters by adult stages of descendants is called paedomorphosis. However, this process can mislead phylogenetic analyses based on morphological data, even in combination with molecular data, because the assessment if a character is primary absent or secondary lost is difficult. Thus, the detection of incongruence between morphological and molecular data is necessary to investigate the reliability of simultaneous analyses. Different methods have been proposed to detect data congruence or incongruence. Five of them (PABA, PBS, NDI, LILD, DRI) are used herein to assess incongruence between morphological and molecular data in a case study addressing salamander phylogeny, which comprises several supposedly paedomorphic taxa. Therefore, previously published data sets were compiled herein. Furthermore, two strategies ameliorating effects of paedomorphosis on phylogenetic studies were tested herein using a statistical rigor. Additionally, efficiency of the different methods to assess incongruence was analyzed using this empirical data set. Finally, a test statistic is presented for all these methods except DRI.ResultsThe addition of morphological data to molecular data results in both different positions of three of the four paedomorphic taxa and strong incongruence, but treating the morphological data using different strategies ameliorating the negative impact of paedomorphosis revokes these changes and minimizes the conflict. Of these strategies the strategy to just exclude paedomorphic character traits seem to be most beneficial. Of the three molecular partitions analyzed herein the RAG1 partition seems to be the most suitable to resolve deep salamander phylogeny. The rRNA and mtDNA partition are either too conserved or too variable, respectively. Of the different methods to detect incongruence, the NDI and PABA approaches are more conservative in the indication of incongruence than LILD and PBS.ConclusionPaedomorphosis induces strong conflicts and can mislead the phylogenetic analyses even in combined analyses. However, different strategies are efficiently minimizing these problems. Though the exploration of different methods to detect incongruence is preferable NDI and PABA are more conservative than the others and NDI is computational less extensive than PABA.
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Project description:Cells can react to their environment by changing the activity of enzymes in response to specific chemical signals. Artificial catalysts capable of being activated by chemical signals are rare, but of interest for creating autonomously responsive materials. We present an organocatalyst that is activated by a chemical signal, enabling temporal control over reaction rates and the formation of materials. Using self-immolative chemistry, we design a deactivated aniline organocatalyst that is activated by the chemical signal hydrogen peroxide and catalyses hydrazone formation. Upon activation of the catalyst, the rate of hydrazone formation increases 10-fold almost instantly. The responsive organocatalyst enables temporal control over the formation of gels featuring hydrazone bonds. The generic design should enable the use of a large range of triggers and organocatalysts, and appears a promising method for the introduction of signal response in materials, constituting a first step towards achieving communication between artificial chemical systems.Enzymes regulated by chemical signals are common in biology, but few such artificial catalysts exist. Here, the authors design an aniline catalyst that, when activated by a chemical trigger, catalyses formation of hydrazone-based gels, demonstrating signal response in a soft material.
| S-EPMC5638897 | biostudies-literature
Project description:This article asks to what extent and under which conditions interest groups are congruent with public opinion. We argue that interest groups can be caught in a balancing act between engaging with their constituency on the one hand and aligning their position with the broader public on the other hand. We contribute to previous studies by arguing that the effect of interest group type on congruence is moderated by the degree to which constituencies are involved in advocacy processes and the salience of policy issues. We test these expectations by analyzing 314 media claims made by Belgian interest groups regarding 58 policy issues. The results demonstrate that citizen groups with formal members are more prone to share the position of the broader public compared to concentrated interest groups such as business associations, especially if they involve their members in advocacy activities and when issues are salient in the media.
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Project description:BACKGROUND:Many biologically active factors are present in human milk including proteins, lipids, immune factors, and hormones. The milk composition varies over time and shows large inter-individual variability. This study examined variations of human milk immune factors and cortisol concentrations in the first three months post-partum, and their potential associations with maternal psychosocial distress. METHODS:Seventy-seven healthy mothers with full term pregnancies were enrolled, of which 51 mothers collected morning milk samples at 2, 6 and 12 weeks post-delivery. Maternal psychosocial distress was assessed at 6 weeks post-delivery using questionnaires for stress, anxiety, and depressive symptoms. Immune factors were determined using multiplex immunoassays and included innate immunity factors (IL1?, IL6, IL12, IFN?, TNF?), acquired immunity factors (IL2, IL4, IL10, IL13, IL17), chemokines (IL8, Gro?, MCP1, MIP1?), growth factors (IL5, IL7, GCSF, GMCSF, TGF?2) and immunoglobulins (IgA, total IgG, IgM). Cortisol was quantified using liquid chromatography-tandem mass spectrometry. A linear mixed effects model was fit to test whether stress, anxiety, and depressive symptoms individually predicted human milk cortisol concentrations after accounting for covariates. Repeated measurement analyses were used to compare women with high (n = 13) versus low psychosocial distress (n = 13) for immune factors and cortisol concentrations. RESULTS:Virtually all immune factors and cortisol, with the exception of the granulocyte-macrophage colony-stimulating factor (GMCSF), were detected in the human milk samples. The concentrations of the immune factors decreased during the first 3 months, while cortisol concentrations increased over time. No correlation was observed between any of the immune factors and cortisol. No consistent relationship between postnatal psychosocial distress and concentrations of immune factors was found, whereas higher psychosocial distress was predictive of higher cortisol concentrations in human milk. CONCLUSION:In the current study we found no evidence for an association between natural variations in maternal distress and immune factor concentrations in milk. It is uncertain if this lack of association would also be observed in studies with larger populations, with less uniform demographic characteristics, or with women with higher (clinical) levels of anxiety, stress and/or depressive symptoms. In contrast, maternal psychosocial distress was positively related to higher milk cortisol concentrations at week 2 post-delivery. Further investigation on maternal psychosocial distress in relation to human milk composition is warranted.
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Project description:Chronic diseases represent a serious threat to public health across the world. It is estimated at about 60% of all deaths worldwide and approximately 43% of the global burden of chronic diseases. Thus, the analysis of the healthcare data has helped health officials, patients, and healthcare communities to perform early detection for those diseases. Extracting the patterns from healthcare data has helped the healthcare communities to obtain complete medical data for the purpose of diagnosis. The objective of the present research work is presented to improve the surveillance detection system for chronic diseases, which is used for the protection of people's lives. For this purpose, the proposed system has been developed to enhance the detection of chronic disease by using machine learning algorithms. The standard data related to chronic diseases have been collected from various worldwide resources. In healthcare data, special chronic diseases include ambiguous objects of the class. Therefore, the presence of ambiguous objects shows the availability of traits involving two or more classes, which reduces the accuracy of the machine learning algorithms. The novelty of the current research work lies in the assumption that demonstrates the noncrisp Rough K-means (RKM) clustering for figuring out the ambiguity in chronic disease dataset to improve the performance of the system. The RKM algorithm has clustered data into two sets, namely, the upper approximation and lower approximation. The objects belonging to the upper approximation are favourable objects, whereas the ones belonging to the lower approximation are excluded and identified as ambiguous. These ambiguous objects have been excluded to improve the machine learning algorithms. The machine learning algorithms, namely, naïve Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), and random forest tree, are presented and compared. The chronic disease data are obtained from the machine learning repository and Kaggle to test and evaluate the proposed model. The experimental results demonstrate that the proposed system is successfully employed for the diagnosis of chronic diseases. The proposed model achieved the best results with naive Bayes with RKM for the classification of diabetic disease (80.55%), whereas SVM with RKM for the classification of kidney disease achieved 100% and SVM with RKM for the classification of cancer disease achieved 97.53 with respect to accuracy metric. The performance measures, such as accuracy, sensitivity, specificity, precision, and F-score, are employed to evaluate the performance of the proposed system. Furthermore, evaluation and comparison of the proposed system with the existing machine learning algorithms are presented. Finally, the proposed system has enhanced the performance of machine learning algorithms.
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