Automated Inference of Shilling Behavior in Online Auction Systems

Auction frauds develop in online auctions as online auction platforms expand in use. Shill bidding is one of the most prevalent forms of auction frauds that violate the integrity of online auctions. A shill is a person who pretends to be a legitimate buyer and feigns enthusiasm for an auctioned item by bidding up the auction price. Although the punishment for auction fraud could be severe (e.g., several years in prison with fines), shill bidding is still very popular. One primary reason is the lack of effective shill detection techniques in current online auction systems. Shill inference in online auctions is a difficult problem due to the characteristic of concealment of shill bidding activities and the anonymous nature of online applications. Shill bidding usually occurs without leaving obvious direct physical evidence, thus it cannot be easily noticed by the victims. In addition, because online auction users do not deal with each other face to face, acquired “hints” or evidence of shilling behavior generally involves uncertainty, thus making the investigation even more challenging. We propose to design an automated and effective approach to infer shills in online auction systems. To assist the investigation, we conducted an empirical study on the relationship between final auction price and shill bidding activity. Based on a predicted price, the actual price can help distinguish trustworthy auctions from likely shill-infected auctions. To infer exact shills, we propose to formalize various auction-level indicators and bid-level indicators that support shill bidding as well as innocent bidding. Since each indicator can involve uncertainty, we employ a formal reasoning technique, Dempster-Shafer (D-S) theory, to model the uncertainties associated with different indicators that pertain to varied aspects of an auction. This allows us to explicitly represent the uncertainties and combine knowledge from different sources to produce an aggregated assessment of trustworthiness

Plot of the first two principal components from Principal Component Analysis (PCA).

<p>The PCA was performed with As, Fe and Mn in DCB extracts, total As in soil, Fe₂O₃ and Mn₂O₃, available phosphorus (AP) and Si (ASi), selected soil properties, As in rice tissues.</p

Location map of the study area and distribution of sampling sites.

<p>Location map of the study area and distribution of sampling sites.</p

Cultivar means for each parameter as observed in Renhua and Lechang.

<p>Results are presented as arithemic mean ± SD; means within a row for a certain genotype grown in Lechang or Renhua followed by different letters are significantly different at the 0.05 level; the comparisons are based on estimated marginal means.</p><p>Cultivar means for each parameter as observed in Renhua and Lechang.</p

Mechanisms Controlling Arsenic Uptake in Rice Grown in Mining Impacted Regions in South China

<div><p>Foods produced on soils impacted by Pb-Zn mining activities are a potential health risk due to plant uptake of the arsenic (As) associated with such mining. A field survey was undertaken in two Pb-Zn mining-impacted paddy fields in Guangdong Province, China to assess As accumulation and translocation, as well as other factors influencing As in twelve commonly grown rice cultivars. The results showed that grain As concentrations in all the surveyed rice failed national food standards, irrespective of As speciation. Among the 12 rice cultivars, “SY-89” and “DY-162” had the least As in rice grain. No significant difference for As concentration in grain was observed between the rice grown in the two areas that differed significantly for soil As levels, suggesting that the amount of As contamination in the soil is not necessarily the overriding factor controlling the As content in the rice grain. The iron and manganese plaque on the root surface curtailed As accumulation in rice roots. Based on our results, the accumulation of As within rice plants was strongly associated with such soil properties such as silicon, phosphorus, organic matter, pH, and clay content. Understanding the factors and mechanisms controlling As uptake is important to develop mitigation measures that can reduce the amount of As accumulated in rice grains produced on contaminated soils.</p></div

Descriptive statistics of soil properties of Renhua and Lechang.

<p>Results are presented as arithemic mean ± SD; probability indicates the differences between Renhua and Lechang; n represents Number of samples; the <i>italic</i> number represents the minimum value of the characteristic in all the 28 surveyed samples; the <b>bold</b> number represents the maximum value of the characteristic in all the 28 surveyed samples.</p><p>Descriptive statistics of soil properties of Renhua and Lechang.</p

Descriptive statistics of rice plant accumulation and transfer factors.

<p>Results are presented as arithemic mean ± SD; probability indicates the differences between Renhua and Lechang; n represents Number of samples; the <i>italic</i> number represents the minimum value of the characteristic in all the 28 surveyed samples; the <b>bold</b> number represents the maximum value of the characteristic in all the 28 surveyed samples.</p><p>Descriptive statistics of rice plant accumulation and transfer factors.</p

Analysis of Differential Gene Expression and Novel Transcript Units of Ovine Muscle Transcriptomes

<div><p>In this study, we characterized differentially expressed genes (DEGs) between the muscle transcriptomes of Small-tailed Han sheep and Dorper sheep and predicted novel transcript units using high-throughput RNA sequencing technology. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that 1,300 DEGs were involved in cellular processes, metabolic pathways, and the actin cytoskeleton pathway. Importantly, we identified 34 DEGs related to muscle cell development and differentiation. Additionally, we were able to optimize the gene structure and predict the untranslated regions (UTRs) for some of the DEGs. Among the 123,678 novel predicted transcript units (TUs), 15,015 units were predicted protein sequences. The reliability of the sequencing data was verified through qRT-PCR analysis of 12 genes. These results will provide useful information for functional genetic research in the future.</p></div

Protein-protein interaction network of the significantly differentially expressed genes.

<p>The sketch represents the interaction network constructed for 1,009 protein-coding DEGs. The red and green dots represent the up-regulated and down-regulated genes, respectively. The 20 proteins related to muscle cell development are underlined in the center of the circle. The lines represent the interactions between the gene products.</p

Table_6_Silicon fertilizer mediated structural variation and niche differentiation in the rhizosphere and endosphere bacterial microbiome and metabolites of sugarcane.XLSX

The microbiomes of plant are potential determinants of plant growth, productivity, and health. They provide plants with a plethora of functional capacities, namely, phytopathogens suppression, access to low-abundance nutrients, and resistance to environmental stressors. However, a comprehensive insight into the structural compositions of the bacterial abundance, diversity, richness, and function colonizing various microenvironments of plants, and specifically their association with bioactive compounds and soil edaphic factors under silicon (Si) amendment remains largely inconclusive. Here, high-throughput sequencing technology and nontargeted metabolite profiling method were adopted to test the hypotheses regarding microbiome niche abundance, diversity, richness, function, and their association with bioactive compounds and soil edaphic factors within different ecological niches (leaf, stem, root, rhizosphere, and bulk soils) under Si amendment during cane growth were we addressed. Our results demonstrated that Si correspondingly increased sugarcane theoretical production and yield, and remarkably enhanced soil nutrient status, especially Si, AP, and AK. It was also observed that bacterial diversity demonstrated tissue-dependent distribution patterns, with the bulk soil, rhizosphere soil, and root endosphere revealing the highest amount of bacterial diversity compared with the stem and leaf tissues. Moreover, Si exhibited the advantage of considerably promoting bacterial abundance in the various plant compartments. Co-occurrence interactions demonstrated that Si application has the potential to increase bacterial diversity maintenance, coexistence, and plant–soil systems bacteria connections, thereby increasing the functional diversity in the various plant tissues, which, in turn, could trigger positive growth effects in plants. Network analysis further revealed that metabolite profiles exhibited a strong association with bacterial community structures. It was also revealed that Si content had a considerable positive association with bacterial structures. Our findings suggest that the dynamic changes in microbe’s community composition in different plant and soil compartments were compartment-specific. Our study provides comprehensive empirical evidence of the significance of Si in agriculture and illuminated on differential metabolite profiles and soil microbe’s relationship.</p