Influence of the Cortical Midline Structures on Moral Emotion and Motivation in Moral Decision-Making

The present study aims to examine the relationship between the cortical midline structures (CMS), which have been regarded to be associated with selfhood, and moral decision making processes at the neural level. Traditional moral psychological studies have suggested the role of moral self as the moderator of moral cognition, so activity of moral self would present at the neural level. The present study examined the interaction between the CMS and other moral-related regions by conducting psycho-physiological interaction analysis of functional images acquired while 16 subjects were solving moral dilemmas. Furthermore, we performed Granger causality analysis to demonstrate the direction of influences between activities in the regions in moral decision-making. We first demonstrate there are significant positive interactions between two central CMS seed regions—i.e., the medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC)—and brain regions associated with moral functioning including the cerebellum, brainstem, midbrain, dorsolateral prefrontal cortex, orbitofrontal cortex and anterior insula (AI); on the other hand, the posterior insula (PI) showed significant negative interaction with the seed regions. Second, several significant Granger causality was found from CMS to insula regions particularly under the moral-personal condition. Furthermore, significant dominant influence from the AI to PI was reported. Moral psychological implications of these findings are discussed. The present study demonstrated the significant interaction and influence between the CMS and morality-related regions while subject were solving moral dilemmas. Given that, activity in the CMS is significantly involved in human moral functioning

Discovering Organizational Correlations from Twitter

Organizational relationships are usually very complex in real life. It is difficult or impossible to directly measure such correlations among different organizations, because important information is usually not publicly available (e.g., the correlations of terrorist organizations). Nowadays, an increasing amount of organizational information can be posted online by individuals and spread instantly through Twitter. Such information can be crucial for detecting organizational correlations. In this paper, we study the problem of discovering correlations among organizations from Twitter. Mining organizational correlations is a very challenging task due to the following reasons: a) Data in Twitter occurs as large volumes of mixed information. The most relevant information about organizations is often buried. Thus, the organizational correlations can be scattered in multiple places, represented by different forms; b) Making use of information from Twitter collectively and judiciously is difficult because of the multiple representations of organizational correlations that are extracted. In order to address these issues, we propose multi-CG (multiple Correlation Graphs based model), an unsupervised framework that can learn a consensus of correlations among organizations based on multiple representations extracted from Twitter, which is more accurate and robust than correlations based on a single representation. Empirical study shows that the consensus graph extracted from Twitter can capture the organizational correlations effectively.Comment: 11 pages, 4 figure

Electrochemically instantaneous reduction of conducting polyaniline-coated latex particles dispersed in acidic solution

A cathodic voltammetric wave was observed in an aqueous suspension of mono-dispersed, spherical polyaniline-coated polystyrene particles, whereas no anodic wave was detected. This irreversibility was common to particles with eight different diameters ranging from 0.2 to 7.5 μm. Such irreversibility cannot be found at polyaniline-coated electrodes, and thus is a property of the dispersion of polyaniline latex. The reduction current was controlled by diffusion of dispersed particles. The reduction, being the conversion from the electrical conducting state to the resistive one, should begin at a point of contact between the conducting particle and the electrode in order to be propagated to the whole particle rapidly. In contrast, the oxidation proceeds slowly with the propagation of conducting zone, during which Brownian motion lets the particle detach from the electrode. The number of loaded aniline units per particle, determined by weight analysis, ranged from 6×10_6 (φ 0.2 μm) to 3×10_11 (φ 7.5 μm) and was proportional to 2.9 powers of the particle diameter. The diffusion-controlled current of the cathodic wave was proportional to 2.4 powers of the diameter. The difference in these powers, 0.5, agreed with a theoretical estimation of the diffusion-controlled current, the diffusion coefficient for which was given by the Stokes-Einstein equation

Statistical Tests for Replacing Human Decision Makers with Algorithms

This paper proposes a statistical framework with which artificial intelligence can improve human decision making. The performance of each human decision maker is first benchmarked against machine predictions; we then replace the decisions made by a subset of the decision makers with the recommendation from the proposed artificial intelligence algorithm. Using a large nationwide dataset of pregnancy outcomes and doctor diagnoses from prepregnancy checkups of reproductive age couples, we experimented with both a heuristic frequentist approach and a Bayesian posterior loss function approach with an application to abnormal birth detection. We find that our algorithm on a test dataset results in a higher overall true positive rate and a lower false positive rate than the diagnoses made by doctors only. We also find that the diagnoses of doctors from rural areas are more frequently replaceable, suggesting that artificial intelligence assisted decision making tends to improve precision more in less developed regions.Comment: 65 pages, 19 figure

Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark]

The field of urban spatial-temporal prediction is advancing rapidly with the development of deep learning techniques and the availability of large-scale datasets. However, challenges persist in accessing and utilizing diverse urban spatial-temporal datasets from different sources and stored in different formats, as well as determining effective model structures and components with the proliferation of deep learning models. This work addresses these challenges and provides three significant contributions. Firstly, we introduce "atomic files", a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets, simplifying data management. Secondly, we present a comprehensive overview of technological advances in urban spatial-temporal prediction models, guiding the development of robust models. Thirdly, we conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions. Overall, this work effectively manages urban spatial-temporal data, guides future efforts, and facilitates the development of accurate and efficient urban spatial-temporal prediction models. It can potentially make long-term contributions to urban spatial-temporal data management and prediction, ultimately leading to improved urban living standards.Comment: 14 pages, 3 figures. arXiv admin note: text overlap with arXiv:2304.1434

Immediate response of paddy soil microbial community and structure to moisture changes and nitrogen fertilizer application

Water and fertilizer managements are the most common practices to maximize crop yields, and their long-term impact on soil microbial communities has been extensively studied. However, the initial response of microbes to fertilization and soil moisture changes remains unclear. In this study, the immediate effects of nitrogen (N)-fertilizer application and moisture levels on microbial community of paddy soils were investigated through controlled incubation experiments. Amplicon sequencing results revealed that moisture had a stronger influence on the abundance and community composition of total soil bacteria, as well as ammonia oxidizing-archaea (AOA) and -bacteria (AOB). Conversely, fertilizer application noticeably reduced the connectivity and complexity of the total bacteria network, and increasing moisture slightly exacerbated these effects. NH4+-N content emerged as a significant driving force for changes in the structure of the total bacteria and AOB communities, while NO3−-N content played more important role in driving shifts in AOA composition. These findings indicate that the initial responses of microbial communities, including abundance and composition, and network differ under water and fertilizer managements. By providing a snapshot of microbial community structure following short-term N-fertilizer and water treatments, this study contributes to a better understanding of how soil microbes respond to long-term agriculture managements

Use of ITS2 Region as the Universal DNA Barcode for Plants and Animals

The internal transcribed spacer 2 (ITS2) region of nuclear ribosomal DNA is regarded as one of the candidate DNA barcodes because it possesses a number of valuable characteristics, such as the availability of conserved regions for designing universal primers, the ease of its amplification, and sufficient variability to distinguish even closely related species. However, a general analysis of its ability to discriminate species in a comprehensive sample set is lacking.In the current study, 50,790 plant and 12,221 animal ITS2 sequences downloaded from GenBank were evaluated according to sequence length, GC content, intra- and inter-specific divergence, and efficiency of identification. The results show that the inter-specific divergence of congeneric species in plants and animals was greater than its corresponding intra-specific variations. The success rates for using the ITS2 region to identify dicotyledons, monocotyledons, gymnosperms, ferns, mosses, and animals were 76.1%, 74.2%, 67.1%, 88.1%, 77.4%, and 91.7% at the species level, respectively. The ITS2 region unveiled a different ability to identify closely related species within different families and genera. The secondary structure of the ITS2 region could provide useful information for species identification and could be considered as a molecular morphological characteristic.)