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Traffic congestion can lead to negative driving emotions, significantly increasing the likelihood of traffic accidents. Reducing negative driving emotions as a means to mitigate speeding, reckless overtaking, and aggressive driving behaviors is a viable approach. Among the potential methods, affective speech has been considered one of the most promising. However, research on humor-based affective speech interventions in the context of driving negative emotions is scarce, and the utilization of electroencephalogram (EEG) signals for emotion detection in humorous audio studies remains largely unexplored. Therefore, our study first designed a highly realistic experiment scenario to induce negative emotions experienced by drivers in congested traffic conditions. Subsequently, we collected drivers' EEG signals and subjective questionnaire ratings during the driving process. By employing one-way analysis of variance (ANOVA) and t tests, we analyzed the data to validate the success of our experiment in inducing negative emotions in drivers during congested road conditions and to assess the effectiveness of humorous audio in regulating drivers' negative emotions. The results indicated that humorous audio effectively alleviated drivers' negative emotions in congested road conditions, with a 145.84% increase in arousal and a 93.55% increase in valence ratings compared to control conditions. However, it should be noted that humorous audio only restored drivers' emotions to the level experienced during normal driving. Our findings offer novel insights into regulating drivers' negative emotions during congested road conditions. [ABSTRACT FROM AUTHOR]

Disease development requires the activation of complex multi-factor processes involving numerous long noncoding RNAs (lncRNAs), which describe non-protein-coding RNAs longer than 200 nucleotides. Emerging evidence indicates that lncRNAs act as essential regulators that perform pivotal roles in the pathogenesis and progression of human diseases. The mechanisms underlying lncRNA involvement in diverse diseases have been extensively explored, and lncRNAs are considered powerful biomarkers for clinical practice. The lncRNA noncatalytic region of tyrosine kinase adaptor protein 1 (NCK1) antisense 1 (NCK1-AS1), also known as NCK1 divergent transcript (NCK1-DT), is encoded on human chromosome 3q22.3 and produces a 27,274-base-long transcript. NCK1-AS1 has increasingly been characterized as a causative agent for multiple diseases. The abnormal expression and involvement of NCK1-AS1 in various biological processes have been associated with several diseases. Further exploration of the mechanisms through which NCK1-AS1 contributes to disease development and progression will provide a foundation for potential clinical applications of NCK1-AS1 in the diagnosis and treatment of various diseases. This review summarizes the current understanding of the various functions and mechanisms through which NCK1-AS1 contributes to various diseases and the clinical application prospects for NCK1-AS1. [ABSTRACT FROM AUTHOR]

Carbonate sands (CS) have the potential to be utilized as construction materials, and their porous particles may bring unique thermal properties, which is critical for the thermal design of geosystems; however, this area of research is highly understudied. The present work provides a new contribution in this field by investigating the thermal conductivity (λ) of five uniform fractions of CS from the South China Sea, with emphasis on the influence of particle size and relative density of the sand. The impact of the size of particles on the index void ratios and thermal conductivity of the samples was profoundly different from that of silica sands. Contrary to silica sands, the extreme void ratio (emax, emin), which is critical for calculation of relative density, of the CS increased as the mean grain size increased. The maximum thermal conductivity of each fraction was negatively correlated with the particle size, and the thermal conductivity of the finer fractions exhibited higher sensitivity to the packing density. Literature models were found to be ineffective in predicting the thermal conductivity of the CS given the unique thermal energy transferring mechanisms of the porous particles. Two thermal conductivity models, stemming from semi-analytical and empirical approaches, were proposed in the light of providing useful guidance for the thermal designs that include CS. [ABSTRACT FROM AUTHOR]

Dimension reduction (DR) algorithms project data from high dimensions to lower dimensions to enable visualization of interesting high-dimensional structure. DR algorithms are widely used for analysis of single-cell transcriptomic data. Despite widespread use of DR algorithms such as t-SNE and UMAP, these algorithms have characteristics that lead to lack of trust: they do not preserve important aspects of high-dimensional structure and are sensitive to arbitrary user choices. Given the importance of gaining insights from DR, DR methods should be evaluated carefully before trusting their results. In this paper, we introduce and perform a systematic evaluation of popular DR methods, including t-SNE, art-SNE, UMAP, PaCMAP, TriMap and ForceAtlas2. Our evaluation considers five components: preservation of local structure, preservation of global structure, sensitivity to parameter choices, sensitivity to preprocessing choices, and computational efficiency. This evaluation can help us to choose DR tools that align with the scientific goals of the user. The authors provide an evaluation framework for dimension reduction methods that illuminates the strengths and weaknesses of different algorithms, and applies this framework to evaluate the PCA, t-SNE, UMAP, TriMap, PaCMAP, ForceAtlas2, and PHATE algorithms. [ABSTRACT FROM AUTHOR]