Finish Them!: Pricing Algorithms for Human Computation

Given a batch of human computation tasks, a commonly ignored aspect is how the price (i.e., the reward paid to human workers) of these tasks must be set or varied in order to meet latency or cost constraints. Often, the price is set up-front and not modified, leading to either a much higher monetary cost than needed (if the price is set too high), or to a much larger latency than expected (if the price is set too low). Leveraging a pricing model from prior work, we develop algorithms to optimally set and then vary price over time in order to meet a (a) user-specified deadline while minimizing total monetary cost (b) user-specified monetary budget constraint while minimizing total elapsed time. We leverage techniques from decision theory (specifically, Markov Decision Processes) for both these problems, and demonstrate that our techniques lead to upto 30\% reduction in cost over schemes proposed in prior work. Furthermore, we develop techniques to speed-up the computation, enabling users to leverage the price setting algorithms on-the-fly

CARMI: A Cache-Aware Learned Index with a Cost-based Construction Algorithm

Learned indexes, which use machine learning models to replace traditional index structures, have shown promising results in recent studies. However, our understanding of this new type of index structure is still at an early stage with many details that need to be carefully examined and improved. In this paper, we propose a cache-aware learned index (CARMI) design to improve the efficiency of the Recursive Model Index (RMI) framework proposed by Kraska et al. and a cost-based construction algorithm to construct the optimal indexes in a wide variety of application scenarios. We formulate the problem of finding the optimal design of a learned index as an optimization problem and propose a dynamic programming algorithm for solving it and a partial greedy step to speed up. Experiments show that our index construction strategy can construct indexes with significantly better performance compared to baselines under various data distribution and workload requirements. Among them, CARMI can obtain an average of 2.52X speedup compared to B-tree, while using only about 0.56X memory space of B-tree on average.Comment: 16 pages, 15 figure

Extracting and utilizing hidden structures in large datasets

The hidden structure within datasets --- capturing the inherent structure within the data not explicitly captured or encoded in the data format --- can often be automatically extracted and used to improve various data processing applications. Utilizing such hidden structure enables us to potentially surpass traditional algorithms that do not take this structure into account. In this thesis, we propose a general framework for algorithms that automatically extract and employ hidden structures to improve data processing performance, and discuss a set of design principles for developing such algorithms. We provide three examples to demonstrate the power of this framework in practice, showcasing how we can use hidden structures to either outperform state-of-the-art methods, or enable new applications that are previously impossible. We believe that this framework can offer new opportunities for the design of algorithms that surpass the current limit, and empower new applications in database research and many other data-centric disciplines

Cytotoxicity of hydroxydihydrobovolide and its pharmacokinetic studies in Portulaca oleracea L. extract

Hydroxydihydrobovolide (HDB) was for the first time isolated from Portulaca oleracea L. and then its cytotoxicity against SH-SYTY cells was studied. Moreover, a rapid and sensitive ultra-high performance liquid chromatographic (UHPLC) method with bergapten as internal standard (IS) was developed and validated to investigate the pharmacokinetics of HDB in rats after intravenous and oral administrations of extract (POE). The UHPLC analysis was performed on a Diamonsil C18 analytical column, using acetonitrile-water (35:65, v/v) as the mobile phase with UV detection at 220 nm. The calibration curve was linear over the range of 0.2-25 µg/mL in rat plasma. The average extraction recovery was from 90.1 to 98.9%, and the relative standard deviations (RSDs) of the intra- and inter-day precisions were less than 4.7 and 4.1%, respectively. The results showed that 50 µM HDB had significant cytotoxicity on the SH-SY5Y cells, which was rapidly distributed with a Tmax of 11 min after oral administration and presented a low absolute bioavailability, 4.12%

Tissue distribution and excretion of the five components of Portulaca oleracea L. extract in rat assessed by UHPLC

The aim of the present study was to investigate the tissue distribution and excretion of five components of Portulaca oleracea L. extract (POE) in rat following oral administration. A rapid, sensitive and specific ultra-high performance liquid chromatography (UHPLC) method with puerarin as the internal standard was used for the quantitative analysis of five components of POE, including caffeic acid (CA), p-coumaric acid (p-CA), ferulic acid (FA), quercitrin (QUER) and hesperidin (HP) in rat tissues including the liver, intestine, stomach, muscle, heart, lung, brain, kidney and spleen, urine and feces. The results show that onlyp-CA and FA were found in nearly all tissues with low cumulative ratios, and CA was higher in the intestine and stomach with a slightly higher cumulative ratio in the urine and feces after 24 h. HP and QUER were found at low levels in the tissues with low cumulative ratios.O objetivo do presente estudo foi investigar a distribuição tecidual e excreção de cinco componentes de extrato Portulaca oleracea L. (POE) em ratos após administração oral. Um método analítico rápido, sensível e específico para quantificação de cinco componentes de POE (ácido cafeico (CA), ácidop-cumárico (p-CA), ácido ferúlico (FA), quercitrina (QUER) e hesperidina (HP)) por cromatografia líquida de ultra eficiência (UHPLC), empregando puerarina como padrão interno de referência. Os compostos foram quantificados em diferentes tecidos dos animais, sendo eles fígado, intestino, estômago, músculo, coração, pulmão, cérebro, rim e baço, urina e fezes. Os resultados mostraram que apenas p-CA e FA foram encontradas em todos os tecidos com baixas taxas cumulativas e CA apresentou níveis mais altos no intestino e estômago com a taxa cumulativa um pouco mais elevada na urina e nas fezes após 24 h. HP e QUER apresentaram baixas concentrações nos tecidos com baixas taxas cumulativas

Immune Infiltration in Atherosclerosis is Mediated by Cuproptosis-Associated Ferroptosis Genes

Aims: In this study, we aimed to identify cuproptosis-associated ferroptosis genes in the atherosclerosis microarray of the Gene Expression Omnibus (GEO) database and to explore hub gene-mediated immune infiltration in atherosclerosis.Background: Immune infiltration plays a crucial role in atherosclerosis development. Ferroptosis is a mode of cell death caused by the iron-dependent accumulation of lipid peroxides. Cuproptosis is a recently discovered type of programmed cell death. No previous studies have examined the mechanism of cuproptosis-associated ferroptosis gene regulation in immune infiltration in atherosclerosis.Methods: We searched the qualified atherosclerosis gene microarray in the GEO database, integrated it with ferroptosis and cuproptosis genes, and calculated the correlation coefficients. We then obtained the cuproptosis-associated ferroptosis gene matrix and screened differentially expressed genes. Subsequently, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses and protein–protein interaction network analysis of differentially expressed genes. We also screened hub genes according to the Matthews correlation coefficient (MCC) algorithm. We conducted enrichment analysis of hub genes to explore their functions and predict related microRNAs (P<0.05). We also used the single-sample gene set enrichment analysis (ssGSEA) algorithm to analyze the relationships between hub genes and immune infiltration, and used immune-associated hub genes to construct a risk model. Finally, we used the drug prediction results and molecular docking technology to explore potential therapeutic drugs targeting the hub genes.Results: Seventy-eight cuproptosis-associated ferroptosis genes were found to be involved in the cellular response to oxidative and chemical stress, and to be enriched in multiple pathways, including ferroptosis, glutathione metabolism, and atherosclerosis. Ten hub genes were identified with the MCC algorithm; according to the ssGSEA algorithm, these genes were closely associated with immune infiltration, thus indicating that cuproptosis-associated ferroptosis genes may participate in atherosclerosis by mediating immune infiltration. The receiver operating characteristic curve indicated that the model had a good ability to predict atherosclerosis risk. The results of drug prediction (adjusted P<0.001) and molecular docking showed that glutathione may be a potential therapeutic drug that targets the hub genes.Conclusion: Cuproptosis-associated ferroptosis genes are associated with immune infiltration in atherosclerosis

Meta-Analysis of Risk Stratification of SCN5A With Brugada Syndrome: Is SCN5A Always a Marker of Low Risk?

Background:SCN5A with Brugada syndrome (BrS) is not commonly considered as an independent risk marker for subsequent cardiac events. However, the risk of SCN5A combined with other clinical characteristics has not been fully investigated.Objectives: The aim of this study is to investigate and evaluate risk stratification and related risk factors of SCN5A in BrS.Methods: The databases of PubMed, EMBASE, Cochrane Library, MEDLINE, Chinese National Knowledge Infrastructure (CNKI) and Wanfang Data were searched for related studies published from January 2002 to May 2018 followed by meta-analysis. The BrS patients who underwent SCN5A gene tests were included. The prognosis and risk stratification of SCN5A combined with symptoms and asymptoms diagnosis in BrS, electrophysiology study (EPS) were then investigated and evaluated. Outcomes were defined as ventricular tachycardia/fibrillation (VT/VF), sudden cardiac death (SCD).Results: Eleven suitable studies involving 1892 BrS patients who underwent SCN5A gene tests were identified. SCN5A (+) was not considered to be a significant predictor of future cardiac events (95% CI: 0.89–2.11; P = 0.15; I2 = 0%). However, SCN5A (+) patients with symptoms at diagnosis revealed a higher prevalence of future VT/VF, SCD compared to SCN5A (–) patients with symptoms at diagnosis. (95% CI: 1.06–3.70; P = 0.03 I2 = 0%) Among asymptomatic patients, the risk did not significantly differ between SCN5A (+) patients and SCN5A (–) patients. (95% CI: 0.51–4.72; P = 0.45 I2 = 0 %). In an investigation involving patients in EPS (–) BrS electrocardiogram (ECG), the risk of SCN5A (+) is higher than that of SCN5A (–) (P &lt; 0.001).Conclusions: In BrS patients with symptoms at diagnosis or EPS (–), the meta-analysis suggests that SCN5A (+) are at a higher risk of arrhythmic events than SCN5A (–)