A budget-limited mechanism for category-aware crowdsourcing systems

Crowdsourcing harnesses human effort to solve computer-hard problems. Such tasks often have different levels of difficulty and workers have varying levels of skill at completing them. With a limited budget, it is important to wisely allocate the spend among the tasks and workers such that the overall outcome is as good as possible. Most existing work addresses this budget allocation problem by assuming that workers have a single level of ability for all tasks. However, this neglects the fact that tasks can belong to a variety of diverse categories and workers may have varying abilities across them. To incorporating such category-awareness, we model the interaction between the crowdsource campaign initiator and the workers as a procurement auction and propose a computationally efficient mechanism, INCARE, to achieve high-quality outcomes given a limited budget. We prove that INCARE is budget feasible, incentive compatible and individually rational. Finally, our experiments on a standard real-world data set show that, compared to the state of the art, INCARE: (i) can improve the accuracy by up to 40%, given a limited budget; and (ii) is significantly more robust to inaccuracies in prior information about each task's difficulty

A budget-limited mechanism for category-aware crowdsourcing systems

Crowdsourcing harnesses human effort to solve computer-hard problems. Such tasks often have different levels of difficulty and workers have varying levels of skill at completing them. With a limited budget, it is important to wisely allocate the spend among the tasks and workers such that the overall outcome is as good as possible. Most existing work addresses this budget allocation problem by assuming that workers have a single level of ability for all tasks. However, this neglects the fact that tasks can belong to a variety of diverse categories and workers may have varying abilities across them. To incorporating such category-awareness, we model the interaction between the crowdsource campaign initiator and the workers as a procurement auction and propose a computationally efficient mechanism, INCARE, to achieve high-quality outcomes given a limited budget. We prove that INCARE is budget feasible, incentive compatible and individually rational. Finally, our experiments on a standard real-world data set show that, compared to the state of the art, INCARE: (i) can improve the accuracy by up to 40%, given a limited budget; and (ii) is significantly more robust to inaccuracies in prior information about each task's difficulty

Supervised subgraph augmented non-negative matrix factorization for interpretable manufacturing time series data analytics

Data analytics has been extensively used for manufacturing time series to reduce process variation and mitigate product defects. However, the majority of data analytics approaches are hard to understand for humans who do not have a data analysis background. Many manufacturing conditions, such as trouble shooting, need situation-dependent responses and are mainly performed by humans. Therefore, it is critical to discover insights from the time series and present those to a human operator in an interpretable format. We propose a novel Supervised Subgraph Augmented Non-negative Matrix Factorization (Super-SANMF) approach to represent and model manufacturing time series. We use a graph representation to approximate a human’s description of time series changing patterns and identify frequent subgraphs as common patterns. The appearances of the subgraphs in the time series are organized in a count matrix, in which each row corresponds to a time series and each column corresponds to a frequent subgraph. Super-SANMF then identifies groups of subgraphs as features that minimize the Kullback–Leibler divergence between measured and approximated matrices. The learned features can yield comparable prediction accuracy (normal or defective) in case studies, compared with the widely used basis expansion approaches (such as spline and wavelet), and are easy for humans to memorize and understand.</p

Table_2_Characterization of the SPI-1 Type III Secretion System in Pseudomonas fluorescens 2P24.XLSX

Pseudomonas fluorescens 2P24 is a plant growth-promoting rhizobacterium (PGPR) isolated from wheat take-all decline soil. Genomic analysis of strain 2P24 revealed the presence of a complete SPI-1 type III secretion system (T3SS) gene cluster on the chromosome with an organization and orientation similar to the SPI-1 T3SS gene clusters of Salmonella enterica and P. kilonensis F113. Phylogenetic analysis revealed that the SPI-1 T3SS gene cluster of strain 2P24 might be obtained from Salmonella and Shigella by horizontal gene transfer. Two transcriptional regulator homologs of HilA and InvF were found from the SPI-1 T3SS gene cluster of strain 2P24. HilA regulated the expression of the structural genes positively, such as invG, sipB, sipD, prgI, and prgK. Prediction of transcriptional binding sites and RNA-seq analysis revealed 14 genes were up-regulated by InvF in strain 2P24. Exploring potential roles of SPI-1 T3SS revealed that it was not associated with motility. However, 2P24ΔinvF reduced resistance against Fusarium graminearum significantly. 2P24ΔhilA enhanced formation of biofilm significantly at 48 h. All three mutants 2P24ΔhilA, 2P24ΔinvF, and 2P24ΔinvE-C reduced the chemotactic responses to glucose significantly. Finally, the determination of SPI-1 mutants to trigger innate immunity in Nicotiana benthamiana showed that 2P24ΔinvE-C reduced the ability to induce the production of reactive oxygen species compared with the wild type strain 2P24.</p

Table_1_Characterization of the SPI-1 Type III Secretion System in Pseudomonas fluorescens 2P24.XLSX

Pseudomonas fluorescens 2P24 is a plant growth-promoting rhizobacterium (PGPR) isolated from wheat take-all decline soil. Genomic analysis of strain 2P24 revealed the presence of a complete SPI-1 type III secretion system (T3SS) gene cluster on the chromosome with an organization and orientation similar to the SPI-1 T3SS gene clusters of Salmonella enterica and P. kilonensis F113. Phylogenetic analysis revealed that the SPI-1 T3SS gene cluster of strain 2P24 might be obtained from Salmonella and Shigella by horizontal gene transfer. Two transcriptional regulator homologs of HilA and InvF were found from the SPI-1 T3SS gene cluster of strain 2P24. HilA regulated the expression of the structural genes positively, such as invG, sipB, sipD, prgI, and prgK. Prediction of transcriptional binding sites and RNA-seq analysis revealed 14 genes were up-regulated by InvF in strain 2P24. Exploring potential roles of SPI-1 T3SS revealed that it was not associated with motility. However, 2P24ΔinvF reduced resistance against Fusarium graminearum significantly. 2P24ΔhilA enhanced formation of biofilm significantly at 48 h. All three mutants 2P24ΔhilA, 2P24ΔinvF, and 2P24ΔinvE-C reduced the chemotactic responses to glucose significantly. Finally, the determination of SPI-1 mutants to trigger innate immunity in Nicotiana benthamiana showed that 2P24ΔinvE-C reduced the ability to induce the production of reactive oxygen species compared with the wild type strain 2P24.</p

Image_1_Characterization of the SPI-1 Type III Secretion System in Pseudomonas fluorescens 2P24.tif

Pseudomonas fluorescens 2P24 is a plant growth-promoting rhizobacterium (PGPR) isolated from wheat take-all decline soil. Genomic analysis of strain 2P24 revealed the presence of a complete SPI-1 type III secretion system (T3SS) gene cluster on the chromosome with an organization and orientation similar to the SPI-1 T3SS gene clusters of Salmonella enterica and P. kilonensis F113. Phylogenetic analysis revealed that the SPI-1 T3SS gene cluster of strain 2P24 might be obtained from Salmonella and Shigella by horizontal gene transfer. Two transcriptional regulator homologs of HilA and InvF were found from the SPI-1 T3SS gene cluster of strain 2P24. HilA regulated the expression of the structural genes positively, such as invG, sipB, sipD, prgI, and prgK. Prediction of transcriptional binding sites and RNA-seq analysis revealed 14 genes were up-regulated by InvF in strain 2P24. Exploring potential roles of SPI-1 T3SS revealed that it was not associated with motility. However, 2P24ΔinvF reduced resistance against Fusarium graminearum significantly. 2P24ΔhilA enhanced formation of biofilm significantly at 48 h. All three mutants 2P24ΔhilA, 2P24ΔinvF, and 2P24ΔinvE-C reduced the chemotactic responses to glucose significantly. Finally, the determination of SPI-1 mutants to trigger innate immunity in Nicotiana benthamiana showed that 2P24ΔinvE-C reduced the ability to induce the production of reactive oxygen species compared with the wild type strain 2P24.</p

Development and Use of Clickable Activity Based Protein Profiling Agents for Protein Arginine Deiminase 4

The protein arginine deiminases (PADs), which catalyze the hydrolysis of peptidyl-arginine to form peptidyl-citrulline, are potential targets for the development of a rheumatoid arthritis (RA) therapeutic, as well as other human diseases including colitis and cancer. Additionally, these enzymes, and in particular PAD4, appear to play important roles in a variety of cell signaling pathways including apoptosis, differentiation, and transcriptional regulation. To better understand the factors that regulate in vivo PAD4 activity, we set out to design and synthesize a series of activity-based protein profiling (ABPP) reagents that target this enzyme. Herein we describe the design, synthesis, and evaluation of six ABPPs including (i) FITC-conjugated F-amidine (FFA1 and 2) and Cl-amidine (FCA1 and 2), and (ii) biotin-conjugated F-amidine (BFA) and Cl-amidine (BCA). We further demonstrate the utility of these probes for labeling PAD4 in cells, as well as for isolating PAD4 and PAD4 binding proteins. These probes will undoubtedly prove to be powerful tools that can be used to dissect the factors controlling the dynamics of PAD4 expression, activity, and function

Qiqilian ameliorates vascular endothelial dysfunction by inhibiting NLRP3-ASC inflammasome activation <i>in vivo</i> and <i>in vitro</i>

Previous studies have highlighted significant therapeutic effects of Qiqilian (QQL) capsule on hypertension in spontaneously hypertensive rats (SHRs); however, its underlying molecular mechanism remains unclear. We investigated the potential mechanism by which QQL improves hypertension-induced vascular endothelial dysfunction (VED). In vivo, SHRs were divided into four groups (20 per group) and were administered gradient doses of QQL (0, 0.3, 0.6, and 1.2 g/kg) for 8 weeks, while Wistar Kyoto rats were used as normal control. The vascular injury extent, IL-1β and IL-18 levels, NLRP3, ASC and caspase-1 contents were examined. In vitro, the effects of QQL-medicated serum on angiotensin II (AngII)-induced inflammatory and autophagy in human umbilical vein endothelial cells (HUVECs) were assessed. Compared with the SHR group, QQL significantly decreased thickness (125.50 to 105.45 μm) and collagen density (8.61 to 3.20%) of arterial vessels, and reduced serum IL-1β (96.25 to 46.13 pg/mL) and IL-18 (345.01 to 162.63 pg/mL) levels. The NLRP3 and ACS expression in arterial vessels were downregulated (0.21- and 0.16-fold, respectively) in the QQL-HD group compared with the SHR group. In vitro, QQL treatment restored NLRP3 and ASC expression, which was downregulated approximately 2-fold compared with that of AngII-induced HUVECs. Furthermore, QQL decreased LC3II and increased p62 contents (p  QQL effectively attenuated endothelial injury and inflammation by inhibiting AngII-induced excessive autophagy, which serves as a potential therapeutic strategy for hypertension.</p

DataSheet_1_Key factors for species distribution modeling in benthic marine environments.docx

Species distribution modeling is a widely used technique for estimating the potential habitats of target organisms based on their environmental preferences. These methods serve as valuable tools for resource managers and conservationists, and their utilization is increasing, particularly in marine environments where data limitations persist as a challenge. In this study, we employed the global distribution predictions of six cold-water coral species as a case study to investigate various factors influencing predictions, including modeling algorithms, background points sampling strategies and sizes, and the collinearity of environmental datasets, using both discriminative and functional performance metrics. The choice of background sampling method exhibits a stronger influence on model performance compared to the effects of modeling algorithms, background point sampling size, and the collinearity of the environmental dataset. Predictions that utilize kernel density backgrounds, maintain an equal number of presences and background points for algorithms of BRT, RF, and MARS, and employ a substantial number of background points for MAXENT, coupled with a collinearity-filtered environmental dataset in species distribution modeling, yield higher levels of discriminative and functional performance. Overall, BRT and RF outperformed MAXENT, a conclusion that is further substantiated by the analysis of smoothed residuals and the uncertainty associated with the predicted habitat suitability of Madrepora oculata. This study offers valuable insights for enhancing species distribution modeling in marine benthic environments, thereby benefiting resource management and conservation strategies for benthic species.</p