A novel pathway-based distance score enhances assessment of disease heterogeneity in gene expression

Distance-based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and the relatively high correlation between genes are often encountered, so traditional distances such as Euclidean distance may not be effective at discriminating the biological differences between samples. In this study, we developed a novel computational method to assess the biological differences based on pathways by assuming that ontologically defined biological pathways in biologically similar samples have similar behavior. Application of this distance score results in more accurate, robust, and biologically meaningful clustering results in both simulated data and real data when compared to traditional methods. It also has comparable or better performance compared to Pathifier

Visual duration aftereffect is position invariant

Adaptation to relatively long or short sensory events leads to a negative aftereffect, such that the durations of the subsequent events within a certain range appear to be contracted or expanded. The distortion in perceived duration is presumed to arise from the adaptation of duration detectors. Here, we focus on the positional sensitivity of those visual duration detectors by exploring whether the duration aftereffect may be constrained by the visual location of stimuli. We adopted two different paradigms, one that tests for transfer across visual hemifields, and the other that tests for simultaneous selectivity between visual hemifields. By employing these experimental designs, we show that the duration aftereffect strongly transfers across visual hemifields and is not contingent on them. The lack of position specificity suggests that duration detectors in the visual system may operate at a relatively later stage of sensory processing

Neural activity dissociation between thought-based and perception-based response conflict

Based on the idea that intentions have different penetrability to perception and thought (Fodor, 1983), four Stroop-like tasks, AA, AW, WA, and WW are used, where the A represents an arrow and the CPPR (closest processing prior to response) is perception, and the W represents a word and the CPPR is thought. Event-related brain potentials were recorded as participants completed these tasks, and sLORETA (standardized low resolution brain electromagnetic tomography) was used to localize the sources at specific time points. These results showed that there is an interference effect in the AA and WA tasks, but not in the AW or WW tasks. The activated brain areas related to the interference effect in the AA task were the PFC and ACC, and PFC activation took place prior to ACC activation; but only PFC in WA task. Combined with previous results, a new neural mechanism of cognitive control is proposed

Visual diagnosis of tree boosting methods

Tree boosting, which combines weak learners (typically decision trees) to generate a strong learner, is a highly effective and widely used machine learning method. However, the development of a high performance tree boosting model is a time-consuming process that requires numerous trial-and-error experiments. To tackle this issue, we have developed a visual diagnosis tool, BOOSTVis, to help experts quickly analyze and diagnose the training process of tree boosting. In particular, we have designed a temporal confusion matrix visualization, and combined it with a t-SNE projection and a tree visualization. These visualization components work together to provide a comprehensive overview of a tree boosting model, and enable an effective diagnosis of an unsatisfactory training process. Two case studies that were conducted on the Otto Group Product Classification Challenge dataset demonstrate that BOOSTVis can provide informative feedback and guidance to improve understanding and diagnosis of tree boosting algorithms

Quantitative Assessment for the Impact of Novel Coronavirus Pneumonia Epidemic on Economic Viability in A Domestic Area

To combine the economic viability of urban areas with a quantitative condition that can characterize “epidemic” and “Pandemic” diseases, we use the factor analysis method to achieve index dimensionality reduction and subjective and objective integration method to achieve optimal weight distribution. We develop a judgment model by taking into account broad factors such as the epidemic situation, population, medical care, economy, and policy. On this basis, we chose 15 common infectious diseases as detection data and used the judgment model to obtain the specific quantitative judgment conditions of “spread, outbreak, epidemic, and pandemic.” The threshold for defining epidemics is between 3 and 5, and the threshold for defining pandemics is greater than 5

The Effects of Same- and Other-Race Facial Expressions of Pain on Temporal Perception

Previous studies suggested that threatening stimuli lengthen subjective duration, while facial expressions of pain were found to produce a shortening effect on temporal perception in a recent study. Moreover, individuals’ responses to others’ pain were influenced by the individuals’ relationship to a racial group. However, the effects of same- and other-race pained facial expressions on temporal perception, remain unknown. The aim of this present study was to identify the effect expressions of pain have on temporal perception and to explore whether this effect was modulated by the relationship to a racial group. In a temporal bisection task, Chinese participants were presented with pain or neutral facial expressions displayed by Caucasian (other-race) or Chinese (same-race) models in a 400–1600 ms or 200–800 ms condition. Expressions of pain were rated as more arousing, negative and disagreeable, than neutral facial expressions. These scores were not significantly different between same- and other-race facial expressions. Based on the results of the temporal bisection task, both same- and other-race pained facial expressions lengthened the perceived duration in the 400–1600 ms condition, but only same-race pained facial expressions produced this effect in the 200–800 ms condition. We postulate that the existence of a short-lived effect of pained facial expressions on lengthening temporal perception caused by arousal and attention, occurs at an earlier time point for same-race pained facial expressions than for other-race pained facial expressions

The impact of stereotype threat on endogenous poverty-elimination dynamics in generationally poor individuals

IntroductionThe study examines the impact of stereotype threat on generationally poor individuals and its effect on achievement motivation. It also explores the extent to which self-affirmation has an intervention effect on the negative impact of stereotype threat.Methods and resultsIn Study 1, statements that contained negative stereotypes were used to elicit stereotype threat in generationally poor individuals; the results show that stereotype threat reduced the performance of generationally poor individuals in a mental-rotation task. Study 2 used a questionnaire to measure the endogenous dynamics of generationally poor individuals attempting to escape poverty after experiencing stereotype threat; participants in the stereotype-threat group showed lower-level endogenous poverty-elimination dynamics than those in the control group. In Study 3, a self-affirmation intervention was administered to the stereotype-threat group after the stereotype threat was induced. Participants in the self-affirmation group were shown to have higher-level endogenous poverty-elimination dynamics than those in the control group.DiscussionThese findings confirm the negative effect of stereotype threat on endogenous poverty-elimination dynamics and verify the effectiveness of self-affirmation in mitigating the negative effects of stereotype threat

A unified understanding of deep NLP models for text classification

The rapid development of deep natural language processing (NLP) models for text classification has led to an urgent need for a unified understanding of these models proposed individually. Existing methods cannot meet the need for understanding different models in one framework due to the lack of a unified measure for explaining both low-level (e.g., words) and high-level (e.g., phrases) features. We have developed a visual analysis tool, DeepNLPVis, to enable a unified understanding of NLP models for text classification. The key idea is a mutual information-based measure, which provides quantitative explanations on how each layer of a model maintains the information of input words in a sample. We model the intra- and inter-word information at each layer measuring the importance of a word to the final prediction as well as the relationships between words, such as the formation of phrases. A multi-level visualization, which consists of a corpus-level, a sample-level, and a word-level visualization, supports the analysis from the overall training set to individual samples. Two case studies on classification tasks and comparison between models demonstrate that DeepNLPVis can help users effectively identify potential problems caused by samples and model architectures and then make informed improvements