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

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

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

Plant commensal type VII secretion system causes iron leakage from roots to promote colonization

Competition for iron is an important factor for microbial niche establishment in the rhizosphere. Pathogenic and beneficial symbiotic bacteria use various secretion systems to interact with their hosts and acquire limited resources from the environment. Bacillus spp. are important plant commensals that encode a type VII secretion system (T7SS). However, the function of this secretion system in rhizobacteria–plant interactions is unclear. Here we use the beneficial rhizobacterium Bacillus velezensis SQR9 to show that the T7SS and the major secreted protein YukE are critical for root colonization. In planta experiments and liposome-based experiments demonstrate that secreted YukE inserts into the plant plasma membrane and causes root iron leakage in the early stage of inoculation. The increased availability of iron promotes root colonization by SQR9. Overall, our work reveals a previously undescribed role of the T7SS in a beneficial rhizobacterium to promote colonization and thus plant–microbe interactions

Identification of general features in soil fungal communities modulated by phenolic acids

Phenolic acids are mainly released from plant residue decomposition and play important roles in the assembly of the soil microbiome. Understanding the general features of soil microbial communities modulated by phenolic acids could provide fundamental insights into the assembly of the soil microbiome. We analyzed the effects of four phenolic acids (ferulic acid, phthalic acid, salicylic acid and tannic acid) on the soil fungal communities at two concentrations. Despite the application of different phenolic acids to the soil, we were able to identify certain general changes in the fungal communities. We found that the tested phenolic acids significantly increased the deterministic assembly process of the fungal community but decreased the fungal diversity. Moreover, the fungal community structure under each tested phenolic acid treatment was distinct at low concentrations but similar at high concentrations. Salicylic acid had the greatest impacts on the fungal community. In addition, Fusarium and Aspergillus were significantly enriched in the soil amended with all the tested phenolic acids at high concentrations. Our study revealed certain general changes in the soil fungal communities modulated by phenolic acids, which deepened our understanding of the fungal assembly mechanism and provided robust insights for identifying candidate phenolic acid-degrading microbes

Large-scale risk prediction applied to Genetic Analysis Workshop 17 mini-exome sequence data

We consider the application of Efron’s empirical Bayes classification method to risk prediction in a genome-wide association study using the Genetic Analysis Workshop 17 (GAW17) data. A major advantage of using this method is that the effect size distribution for the set of possible features is empirically estimated and that all subsequent parameter estimation and risk prediction is guided by this distribution. Here, we generalize Efron’s method to allow for some of the peculiarities of the GAW17 data. In particular, we introduce two ways to extend Efron’s model: a weighted empirical Bayes model and a joint covariance model that allows the model to properly incorporate the annotation information of single-nucleotide polymorphisms (SNPs). In the course of our analysis, we examine several aspects of the possible simulation model, including the identity of the most important genes, the differing effects of synonymous and nonsynonymous SNPs, and the relative roles of covariates and genes in conferring disease risk. Finally, we compare the three methods to each other and to other classifiers (random forest and neural network)

Collapsing-based and kernel-based single-gene analyses applied to Genetic Analysis Workshop 17 mini-exome data

Recently there has been great interest in identifying rare variants associated with common diseases. We apply several collapsing-based and kernel-based single-gene association tests to Genetic Analysis Workshop 17 (GAW17) rare variant association data with unrelated individuals without knowledge of the simulation model. We also implement modified versions of these methods using additional information, such as minor allele frequency (MAF) and functional annotation. For each of four given traits provided in GAW17, we use the Bayesian mixed-effects model to estimate the phenotypic variance explained by the given environmental and genotypic data and to infer an individual-specific genetic effect to use directly in single-gene association tests. After obtaining information on the GAW17 simulation model, we compare the performance of all methods and examine the top genes identified by those methods. We find that collapsing-based methods with weights based on MAFs are sensitive to the “lower MAF, larger effect size” assumption, whereas kernel-based methods are more robust when this assumption is violated. In addition, many false-positive genes identified by multiple methods often contain variants with exactly the same genotype distribution as the causal variants used in the simulation model. When the sample size is much smaller than the number of rare variants, it is more likely that causal and noncausal variants will share the same or similar genotype distribution. This likely contributes to the low power and large number of false-positive results of all methods in detecting causal variants associated with disease in the GAW17 data set

Composition, function and succession of bacterial communities in the tomato rhizosphere during continuous cropping

The bacteria that dominate and become enriched in the rhizosphere during continuous cropping are of increasing interest, as they can greatly adapt to the rhizosphere. However, there are still little knowledge about the general composition and function of these bacteria. In this study, we planted tomatoes in three different soils for three planting cycles and used both high-throughput sequencing and culture-dependent workflows. Despite significant differences in bacterial communities from the initial soils, we observed a similar succession in the rhizosphere bacterial community compositions. We identified certain bacteria that were gradually enriched and potentially beneficial, such as Rhizobium and Flavobacterium. However, some other potentially beneficial bacteria, such as Massilia and Lysobacter, were gradually depleted. Additionally, we found that predicted functions related to xenobiotic biodegradation, nutrient metabolism and antibiotic biosynthesis were enriched in different rhizosphere soils. Beijerinckia fluminensis GR2, which was gradually enriched in all tested soils, significantly inhibited the growth of Ralstonia solanacearum and protected the host from infection. Our study provides new insights into the assembly mechanism of gradually enriched bacteria and their role as plant-beneficial microbes that adapt well to the rhizosphere

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups