The cognitive mechanisms involved in the “DEGREE ADVERB + PROPER NAME” construction: Evaluating proposals from Construction Grammar and Formal Semantics

There are broad disagreements between existing models regarding the mental representations and processes involved in the "DEGREE ADVERB + PROPER NAME" construction, including disagreements regarding the semantics of the degree device, the category status of the proper name, the construction’s expressed meaning and its (non-)compositionality, and, crucially, the operation that holds between the degree device and the proper name. Our corpus-based investigation into two competing models from Construction Grammar and Formal Semantics shows that these models collectively make useful contributions to the scientific understanding of this construction, but neither is empirically adequate. Most importantly, we find that the construction participates in several non-predicted expressed meanings; multivariate analyses show that the three amenable to statistical analysis cluster with different semantic usage-features. We argue that the best way to account for the construction’s semantics-pragmatics is via a previously-dismissed cognitive mechanism: an enrichment-/strengthening-type operation whereby a pragmatically-supplied scale is added to the message.

Detection and Localization of Anamoly in Videos Using Fruit Fly Optimization-Based Self Organized Maps

Detection of anomalies in crowded videos has become an eminent field of research in the community of computer vision. Variation in scene normalcy obtained by training labeled and unlabelled data is identified as Anomaly by diverse traditional approaches. There is no hardcore isolation among anomalous and non-anomalous events; it can mislead the learning process. This paper plans to develop an efficient model for anomaly detection in crowd videos. The video frames are generated for accomplishing that, and feature extraction is adopted. The feature extraction methods like Histogram of Oriented Gradients (HOG) and Local Gradient Pattern (LGP) are used. Further, the meta-heuristic training-based Self Organized Map (SOM) is used for detection and localization. The training of SOM is enhanced by the Fruit Fly Optimization Algorithm (FOA). Moreover, the flow of objects and their directions are determined for localizing the anomaly objects in the detected videos. Finally, comparing the state-of-the-art techniques shows that the proposed model outperforms most competing models on the standard video surveillance dataset.

Emergence of a Distinct Picobirnavirus Genotype Circulating in Patients Hospitalized with Acute Respiratory Illness

Picobirnaviruses (PBV) are found in a wide range of hosts and typically associated with gastrointestinal infections in immunocompromised individuals. Here, a divergent PBV genome was assembled from a patient hospitalized for acute respiratory illness (ARI) in Colombia. The RdRp protein branched with sequences previously reported in patients with ARI from Cambodia and China. Sputa from hospitalized individuals (n = 130) were screened by RT-qPCR which enabled detection and subsequent metagenomic characterization of 25 additional PBV infections circulating in Colombia and the US. Phylogenetic analysis of RdRp highlighted the emergence of two dominant lineages linked to the index case and Asian strains, which together clustered as a distinct genotype. Bayesian inference further established capsid and RdRp sequences as both significantly associated with ARI. Various respiratory-tropic pathogens were detected in PBV+ patients, yet no specific bacteria was common among them and four individuals lacked co-infections, suggesting PBV may not be a prokaryotic virus nor exclusively opportunistic, respectively. Competing models for the origin and transmission of this PBV genotype are presented that attempt to reconcile vectoring by a bacterial host with human pathogenicity. A high prevalence in patients with ARI, an ability to reassort, and demonstrated global spread indicate PBV warrant greater public health concern.

A Novel Feature-Engineered–NGBoost Machine-Learning Framework for Fraud Detection in Electric Power Consumption Data

This study presents a novel feature-engineered–natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-processing, feature engineering, and model evaluation. It utilized the random forest algorithm-based imputation technique initially to impute the missing data entries in the acquired smart meter dataset. In the second phase, the majority weighted minority oversampling technique (MWMOTE) algorithm was used to avoid an unequal distribution of data samples among different classes. The time-series feature-extraction library and whale optimization algorithm were utilized to extract and select the most relevant features from the kWh reading of consumers. Once the most relevant features were acquired, the model training and testing process was initiated by using the NGBoost algorithm to classify the consumers into two distinct categories (“Healthy” and “Theft”). Finally, each input feature’s impact (positive or negative) in predicting the target variable was recognized with the tree SHAP additive-explanations algorithm. The proposed framework achieved an accuracy of 93%, recall of 91%, and precision of 95%, which was greater than all the competing models, and thus validated its efficacy and significance in the studied field of research.

Humor, Empathy, and Community in Twentieth-Century American Poetry

Humor, Empathy, and Community in Twentieth-Century American Poetry argues that American poets of the last hundred years use laughter to promote recognition of shared humanity across difference. Freud and Bergson argue that laughter patrols the boundary between in-group and out-group, but laughter can also help us cross or re-draw that boundary, creating a more democratic understanding of shared experience. Poets’ uses of humor reveal and reinforce deep-seated beliefs about the possibility of empathic mutual understanding among unlike interlocutors. These beliefs also shape poets’ senses of audience and their attitudes toward the notion that poets are somehow exceptional. When poets use humor to promote empathy, they make a claim about the basic ethical function of poetry, because humor and poetry share fundamental structures: both combine disparate subjects into newly meaningful wholes. Taking W. H. Auden and Marianne Moore on one side and Ezra Pound and T. S. Eliot on the other as competing models of how humor can embrace, exclude, and transform, the book charts a developing poetics of laughter in the twentieth and twenty-first centuries through the work of Sterling Brown, Elizabeth Bishop, Stephanie Burt, Cathy Park Hong, and Lucille Clifton, among others. Poets whose race, gender, sexual orientation, or experimentalism place them outside the American mainstream are especially interested in humor’s potential to transcend the very differences it demarcates. Such writers increasingly replace mockery, satire, and other humorous attacks with comic forms that heighten readers’ understanding of and empathy with individuals, while revealing the failures of dominant hierarchical moral and logical systems.

An mRNA silencing mechanism reliant on the cooperation between REGE-1/Regnase-1 and RLE-1/Roquin-1

Regnase-1 is an evolutionarily conserved endoribonuclease, degrading diverse mRNAs important, among others, for immune homeostasis, development, and cancer. There are two competing models of Regnase-1 mediated mRNA silencing. One model postulates that Regnase-1 works together with another RNA-binding protein, Roquin-1. The other model proposes that the two proteins function separately. Studying the C. elegans Regnase-1 ortholog, REGE-1, we have uncovered a functional relationship between REGE-1 and the nematode counterpart of Roquin-1, RLE-1. While REGE-1 and RLE-1 associate with mRNA independently of each other, both proteins are essential for mRNA silencing. Intriguingly, the functional interdependence between REGE-1 and RLE-1 is reminiscent of the proposed cooperation between mammalian Regnase-1 and Roquin-1, which may underlie a prototypic silencing mechanism involving both proteins.

A Bifactor Model of Trait Trust, Distrust, and Suspicion

We explored competing models using bifactor item response theory (IRT) analyses to determine the relationship between trait measures of trust, distrust, and suspicion. The model with a general factor for all three scales fits the data best. We explored the relationship of the emergent general factor by correlating it with two latent traits: Agreeableness and the Trust facet of Agreeableness. The exploratory findings showed evidence that the general factor from the best-fitting model was practically identical to the Trust facet of Agreeableness. We concluded that trait trust, distrust, and suspicion reside on a continuum represented by the general factor, which is dispositional trust.

A Construct Revalidation of the Community of Inquiry Survey: Empirical Evidence for a General Factor Under a Bifactor Structure

The study revisited the community of inquiry (CoI) instrument for construct revalidation. To that end, the study used confirmatory factor analysis (CFA) to examine four competing models (unidimensional, correlated-factor, second-order factor, and bifactor models) on model fit statistics computed using parameter estimates from a statistical estimator for ordinal categorical data. The CFA identified as the optimal structure the bifactor model where all items loaded on their intended domains and the existence of the general factor was supported, essentially evidence of construct validity for the instrument. The study further examined the bifactor model using mostly model-based reliability measures. The findings confirmed the contributions of the general factor to the reliability of instrument scores. The study concluded with validity and reliability evidence for the bifactor model, supported the model as a valid and reliable representation of the CoI instrument and a fuller representation of the CoI theoretical framework, and recommended its use in CoI-related research and practice in online education.

Ancient uniparental DNAs in distinguishing the competing theories of molecular evolution and modern human origins

Analyses of extant people have resulted in two models for the uniparental DNA phylogenetic trees of modern humans rooted in either Africa or East Asia. The Africa model is based on the neutral theory. The Asia model is reached from the maximum genetic diversity (MGD) theory. To test the two competing theories, we examined published data of ancient uniparental DNAs. Many ancient samples belonging to a terminal haplogroup were found to have mutated only in some, but not all, of the sites that define a more basal haplogroup. This pattern was found for the non-controversial haplogroups shared by the two competing models, and also for the haplogroups specific to the Asia model. Furthermore, many ancient samples that do not belong to some of the haplogroups of the Africa model nonetheless had mutations in them, which makes it impossible to unambiguously assign them to a haplogroup within the Africa model. Finally, uniparental DNAs of archaic humans were found to carry some modern alleles present in the first uniparental DNAs in the Asia model, indicating convergent evolution. Therefore, the data from ancient DNAs have verified the MGD theory and the actual existence of the haplogroups specific to the Asia model.

Understanding of Comorbidities Using Modeling Techniques on EHR

Comorbidities refer to the existence of numerous, co-occurring diseases in medicine. The course of one comorbidity is typically extremely dependent on the course of the other condition due to their co-occurrence, and therapies can have major spill-over effects. Despite the high occurrence of comorbidities among patients, there is no complete statistical framework for modelling comorbidity longitudinal dynamics. We propose a probabilistic approach for studying comorbidity dynamics in patients over time in this paper. This approach is a non-homogenous transition technique/mechanism using Hidden Markov Model called as coupled-HMM. Clinical research influenced the design of our coupled-HMM: (1) It accounts for different disease stages (acute, stable) in disease progression by providing clinically meaningful latent phases. (2) It simulates a relationship between the trajectories of comorbidities and the dynamics of capturing co-evolution. (3) The transition mechanism takes into account between-patient heterogeneity (e.g., risk factors, treatments). Based on 675 health trajectories, we assessed our proposed Coupled-HMM, which investigates the concomitant evolution of diabetes mellitus and chronic liver disease. We find that our Coupled-HMM provides a superior fit when compared to competing models without coupling. We also assess the spill-over impact, or the amount to which diabetic therapies are linked to a shift in chronic liver disease from an acute to a stable condition. Immediate application in treatment planning and clinical research becomes possible as a result of our approach in context of comorbidities.