East Slavic indefinite pronouns: a corpus-based approach

The paper focuses on the development and functional distribution of indefinite pronouns in Old East Slavic, taking into account different sources, genres and registers. All the examples in the collected dataset were taken from the historical modules of the Russian National Corpus. They were tagged for type of indefinite marker, source (including originality and date), type of reference of the indefinite marker, semantics, type of discourse, and the degree of formality (formal or informal) present in the context. We then applied both descriptive and inferential statistical methods such as Random Forest analysis as well as multinomial logistic regression. Our analysis enabled us to identify the primary and secondary predictors of the choice of a particular indefinite marker and to trace the functional distribution of indefinite markers according to these factors.

Predicting current voting intentions by Big Five personality domains, facets, and nuances – A random forest analysis approach in a German sample

To understand what was driving individual differences in voting intentions in a large German sample, we investigated the predictability of voting intentions from the Big Five personality domains, facets, and nuances, thereby tackling shortcomings of previous studies. Using random forest analyses in a dataset of N = 4,286 individuals (46.01% men), separate models were trained to predict intentions to 1) not vote versus to vote, 2) vote for a specific party, and 3) vote for a left- versus right-from-the-center party from either the Big Five personality domains, facets, or nuances (represented by individual items). Except for intentions to not vote versus to vote, balanced accuracies to predict voting intentions marginally exceeded those achieved by a baseline learner always predicting the majority class. Using nuances over facets and domains slightly increased balanced accuracies. Results indicate that additional variables should be considered to accurately predict voting intentions, at least in German samples.

Comprehensive Analysis of NAFLD and the Therapeutic Target Identified

Objective: Non-alcoholic fatty liver disease (NAFLD) is a serious health threat worldwide. The aim of this study was to comprehensively describe the metabolic and immunologic characteristics of NAFLD, and to explore potential therapeutic drug targets for NAFLD.Methods: Six NAFLD datasets were downloaded from the Gene Expression Omnibus (GEO) database, including GSE48452, GSE63067, GSE66676, GSE89632, GSE24807, and GSE37031. The datasets we then used to identify and analyze genes that were differentially expressed in samples from patients with NAFLD and normal subjects, followed by analysis of the metabolic and immunologic characteristics of patients with NAFLD. We also identified potential therapeutic drugs for NAFLD using the Connectivity Map (CMAP) database. Moreover, we constructed a prediction model using minimum depth random forest analysis and screened for potential therapeutic targets. Finally, therapeutic targets were verified in a fatty liver model stimulated by palmitic acid (PA).Results: A total of 1,358 differentially expressed genes (DEGs) were obtained, which were mainly enriched in carbohydrate metabolism, lipid metabolism, and other metabolic pathways. Immune infiltration analysis showed that memory B cells, regulatory T cells and M1 macrophage were significantly up-regulated, while T cells follicular helper were down regulated in NAFLD. These may provide a reference for the immune-metabolism interaction in the pathogenesis of NAFLD. Digoxin and helveticoside were identified as potential therapeutic drugs for NAFLD via the CMAP database. In addition, a five-gene prediction model based on minimum depth random forest analysis was constructed, and the receiver operating characteristic (ROC) curves of both training and validation set reached 1. The five candidate therapeutic targets were ENO3, CXCL10, INHBE, LRRC31, and OPTN. Moreover, the efficiency of hepatocyte adipogenesis decreased after OPTN knockout, confirming the potential use of OPTN as a new therapeutic target for NAFLD.Conclusion: This study provides a deeper insight into the molecular pathogenesis of NAFLD. We used five key genes to construct a diagnostic model with a strong predictive effect. Therefore, these five key genes may play an important role in the diagnosis and treatment of NAFLD, particularly those with increased OPTN expression.

Kinematics and observer-animator kinematic similarity predict mental state attribution from Heider–Simmel style animations

The ability to ascribe mental states, such as beliefs or desires to oneself and other individuals forms an integral part of everyday social interaction. Animations tasks, in which observers watch videos of interacting triangles, have been extensively used to test mental state attribution in a variety of clinical populations. Compared to control participants, individuals with clinical conditions such as autism typically offer less appropriate mental state descriptions of such videos. Recent research suggests that stimulus kinematics and movement similarity (between the video and the observer) may contribute to mental state attribution difficulties. Here we present a novel adaptation of the animations task, suitable to track and compare animation generator and -observer kinematics. Using this task and a population-derived stimulus database, we confirmed the hypotheses that an animation’s jerk and jerk similarity between observer and animator significantly contribute to the correct identification of an animation. By employing random forest analysis to explore other stimulus characteristics, we reveal that other indices of movement similarity, including acceleration- and rotation-based similarity, also predict performance. Our results highlight the importance of movement similarity between observer and animator and raise new questions about reasons why some clinical populations exhibit difficulties with this task.

Association Between Dietary Inflammatory Index and Heart Failure: Results From NHANES (1999–2018)

Objective: To explore the relationship between dietary inflammatory index (DII) and heart failure (HF) in participants with cardiovascular and cerebrovascular diseases.Methods: NHANES (1998–2018) data were collected and used to assess the association of HF with DII. Twenty-four-hour dietary consumptions were used to calculate the scores of DII. Demographic characteristics and physical and laboratory examinations were collected for the comparison between HF and non-HF groups. Logistic regression analysis and random forest analysis were performed to calculate the odds rate and determine the potential beneficial dietary components in HF.Results: A total of 19,067 cardiac-cerebral vascular disease participants were categorized as HF (n = 1,382; 7.25%) and non-HF (n = 17,685; 92.75%) groups. Heart failure participants had higher levels of DII score compared with those in the non-HF group (0.239 ± 1.702 vs. −0.145 ± 1.704, p < 0.001). Compared with individuals with T1 (DII: −3.884 to −0.570) of DII, those in T3 (DII: 1.019 to 4.598) had a higher level of total cholesterol (4.49 ± 1.16 vs. 4.75 ± 1.28 mmol/L, p < 0.01), globulin (29.92 ± 5.37 vs. 31.29 ± 5.84 g/L, p < 0.001), and pulse rate (69.90 ± 12.22 vs. 72.22 ± 12.77, p < 0.001) and lower levels of albumin (40.76 ± 3.52 vs. 39.86 ± 3.83 g/L, p < 0.001), hemoglobin (13.76 ± 1.65 vs. 13.46 ± 1.77 g/dl, p < 0.05), and hematocrit (40.83 ± 4.69 vs. 40.17 ± 5.01%, p < 0.05). The odds rates of HF for DII from the logistic regression were 1.140, 1.158, and 1.110 in models 1, 2, and 3, respectively. In addition, from the results of random forest analysis, dietary magnesium, fiber, and beta carotene may be essential in HF.Conclusion: Dietary inflammatory index was positively associated with HF in US adults, and dietary intervention might be a promising method in the therapy of HF.