Cost and Quality Assurance in Crowdsourcing Workflows (Extended Abstract)

International audienceDespite recent advances in artificial intelligence and machine learning, many tasks still require human contributions. With the growing availability of Internet, it is now possible to hire workers on crowdsourcing marketplaces. Many crowdsourcing platforms have emerged in the last decade: Amazon Mechanical Turk, Figure Eight 2 , Wirk 3 , etc. A platform allows employers to post tasks, that are then realized by workers hired from the crowd in exchange for some incentives [3, 19]. Common tasks include image annotation, surveys, classification, recommendation, sentiment analysis, etc. [7]. The existing platforms support simple, repetitive and independent micro-tasks which require a few minutes to an hour to complete. However, many real-world problems are not simple micro-tasks, but rather complex orchestrations of dependent tasks, that process input data and collect human expertize. Existing platforms provide interfaces to post micro-tasks to a crowd, but cannot handle complex tasks. The next stage of crowdsourcing is to build systems to specify and execute complex tasks over existing crowd platforms. A natural solution is to use workflows, i.e., orchestrations of phases that exchange data to achieve a final objective. Figure 1 is an example of complex workflow depicting the image annotation process on SPIPOLL [5], a platform to survey populations of pollinating insects. Contributors take pictures of insects that are then classified by crowdworkers. Pictures are grouped in a dataset , input to node 0. is filtered to eliminate bad pictures (fuzzy, blurred,...) in phase 0. The remaining pictures are sent to workers who try to classify them. If classification is too difficult, the image is sent to an expert. Initial classification is represented by phase 1 in the workflow, and expert classification by 2. Pictures that were discarded, classified easily or studied by experts are then assembled in a result dataset in phase , to do statistics on insect populations. Workflows alone are not sufficient to crowdsource complex tasks. Many data-centric applications come with budget and quality constraints: As human workers are prone to errors, one has to hire several workers to aggregate a final answer with sufficient confidence. An unlimited budget allows hiring large pools of workers to assemble reliable answers for each micro-task, but in general, a client for a complex task has a limited budget. This forces to replicate micro-tasks in an optimal way to achieve the best possible quality, but without exhausting the given budget. The objective is hence to obtain a reliable result, forged through a complex orchestration, at a reasonable cost. Several works consider data centric models, deployment on crowdsourcing platforms, and aggregation techniques to improve data quality (see [11] for a more complete bibliography). First, coordination of tasks has been considered in languages such as BPM

Data Centric Workflows for Crowdsourcing

International audienc

MTHFR 677C>T polymorphism and the risk of breast cancer: evidence from an original study and pooled data for 28031 cases and 31880 controls

Background: Methylenetetrahydrofolate Reductase (MTHFR) acts at an important metabolic point in the regulation of cellular methylation reaction. It assists in the conversion of 5, 10-methylenetetrahydrofolate to 5-methyltetrahydrofolate. The latter aids in remethylation of homocysteine to de novo methionine that is required for DNA synthesis. The objective of this study was to examine the effect of MTHFR 677 C>T polymorphism on the risk of breast cancer in the Indian sub-continent. Methods and Results: We genotyped 677 C>T locus in 1096 individuals that were classified into cases (N = 588) and controls (N = 508). Genotype data were analyzed using chi-square test. No significant difference was observed in the distribution of genotypes between cases and controls in north Indian (P = 0.932), south Indian (P = 0.865), and pooled data (P = 0.680). To develop a consensus regarding the impact of 677C>T polymorphism on breast cancer risk, we also conducted a meta-analysis on 28031 cases and 31880 controls that were pooled from sixty one studies. The overall summary estimate upon meta-analysis suggested no significant correlation between the 677C>T substitution and breast cancer in the dominant model (Fixed effect model: OR = 0.97, P = 0.072, Random effects model: OR = 0.96, P = 0.084) or the recessive model (Fixed effect model: OR = 1.05, P = 0.089; Random effects model: OR = 1.08, P = 0.067). Conclusion: 677 C>T substitution does not affect breast cancer risk in the Indo-European and Dravidian populations of India. Analysis on pooled data further ruled out association between the 677 C>T polymorphism and breast cancer. Therefore, 677 C>T substitution does not appear to influence the risk of breast cancer

Strong impact of TGF-β1 gene polymorphisms on breast cancer risk in Indian women: a case-control and population-based study

Introduction: TGF-β1 is a multi-functional cytokine that plays an important role in breast carcinogenesis. Critical role of TGF-β1 signaling in breast cancer progression is well documented. Some TGF-β1 polymorphisms influence its expression; however, their impact on breast cancer risk is not clear. Methods: We analyzed 1222 samples in a candidate gene-based genetic association study on two distantly located and ethnically divergent case-control groups of Indian women, followed by a population-based genetic epidemiology study analyzing these polymorphisms in other Indian populations. The c.29C>T (Pro10Leu, rs1982073 or rs1800470) and c.74G>C (Arg25Pro, rs1800471) polymorphisms in the TGF-β1 gene were analyzed using direct DNA sequencing, and peripheral level of TGF-β1 were measured by ELISA. Results: c.29C>T substitution increased breast cancer risk, irrespective of ethnicity and menopausal status. On the other hand, c.74G>C substitution reduced breast cancer risk significantly in the north Indian group (p  =  0.0005) and only in the pre-menopausal women. The protective effect of c.74G>C polymorphism may be ethnicity-specific, as no association was seen in south Indian group. The polymorphic status of c.29C>T was comparable among Indo-Europeans, Dravidians and Tibeto-Burmans. Interestingly, we found that Tibeto-Burmans lack polymorphism at c.74G>C locus as true for the Chinese populations. However, the Brahmins of Nepal (Indo-Europeans) showed polymorphism in 2.08% of alleles. Mean TGF-β1 was significantly elevated in patients in comparison to controls (p<0.001). Conclusion: c.29C>T and c.74G>C polymorphisms in the TGF-β1 gene significantly affect breast cancer risk, which correlates with elevated TGF-β1 level in the patients. The c.29C>T locus is polymorphic across ethnically different populations, but c.74G>C locus is monomorphic in Tibeto-Burmans and polymorphic in other Indian populations

Immuno-informatics analysis predicts B and T cell consensus epitopes for designing peptide vaccine against SARS-CoV-2 with 99.82% global population coverage.

The current global pandemic due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has taken a substantial number of lives across the world. Although few vaccines have been rolled-out, a number of vaccine candidates are still under clinical trials at various pharmaceutical companies and laboratories around the world. Considering the intrinsic nature of viruses in mutating and evolving over time, persistent efforts are needed to develop better vaccine candidates. In this study, various immuno-informatics tools and bioinformatics databases were deployed to derive consensus B-cell and T-cell epitope sequences of SARS-CoV-2 spike glycoprotein. This approach has identified four potential epitopes which have the capability to initiate both antibody and cell-mediated immune responses, are non-allergenic and do not trigger autoimmunity. These peptide sequences were also evaluated to show 99.82% of global population coverage based on the genotypic frequencies of HLA binding alleles for both MHC class-I and class-II and are unique for SARS-CoV-2 isolated from human as a host species. Epitope number 2 alone had a global population coverage of 98.2%. Therefore, we further validated binding and interaction of its constituent T-cell epitopes with their corresponding HLA proteins using molecular docking and molecular dynamics simulation experiments, followed by binding free energy calculations with molecular mechanics Poisson-Boltzmann surface area, essential dynamics analysis and free energy landscape analysis. The immuno-informatics pipeline described and the candidate epitopes discovered herein could have significant impact upon efforts to develop globally effective SARS-CoV-2 vaccines

A panel of blood-based circulatory miRNAs with diagnostic potential in patients with psoriasis

Psoriasis is a chronic inflammatory skin disease with keratinocyte hyperproliferation and T cells as key mediators of lesional and systemic inflammatory changes. To date, no suitable differential biomarkers are available for the disease diagnosis. More recently, microRNAs have been identified as critical regulators of lesional and systemic immune changes in psoriasis with diagnostic potential. We have performed expression profiling of T cell-specific miRNAs in 38 plasma samples from psoriasis vulgaris patients and an equal number of age- and gender-matched healthy subjects. Our findings have identified a panel of five blood-based circulatory miRNAs with a significant change in their expression levels, comprising miR-215, miR-148a, miR-125b-5p, miR-223, and miR-142-3p, which can differentiate psoriasis vulgaris patients from healthy individuals. The receiver operating characteristic (ROC) curves for all five miRNAs individually and in combination exhibited a significant disease discriminatory area under the curve with an AUC of 0.762 and a p < 0.0001 for all the miRNAs together. Statistically, all five miRNAs in combination depicted the best-fit model in relation to disease severity (PASI) compared with individual miRNAs, with the highest R2 value of 0.94 and the lowest AIC score of 131.8. Each of the miRNAs also exhibited a significant association with at least one of the other miRNAs in the panel. Importantly, the five miRNAs in the panel regulate one or more immune-inflammation pathways based on target prediction, pathway network analysis, and validated roles in the literature. The miRNA panel provides a rationalized combination of biomarkers that can be tested further on an expanded cohort of patients for their diagnostic value

Immuno-informatics analysis predicts B and T cell consensus epitopes for designing peptide vaccine against SARS-CoV-2 with 99.82% global population coverage.

The current global pandemic due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has taken a substantial number of lives across the world. Although few vaccines have been rolled-out, a number of vaccine candidates are still under clinical trials at various pharmaceutical companies and laboratories around the world. Considering the intrinsic nature of viruses in mutating and evolving over time, persistent efforts are needed to develop better vaccine candidates. In this study, various immuno-informatics tools and bioinformatics databases were deployed to derive consensus B-cell and T-cell epitope sequences of SARS-CoV-2 spike glycoprotein. This approach has identified four potential epitopes which have the capability to initiate both antibody and cell-mediated immune responses, are non-allergenic and do not trigger autoimmunity. These peptide sequences were also evaluated to show 99.82% of global population coverage based on the genotypic frequencies of HLA binding alleles for both MHC class-I and class-II and are unique for SARS-CoV-2 isolated from human as a host species. Epitope number 2 alone had a global population coverage of 98.2%. Therefore, we further validated binding and interaction of its constituent T-cell epitopes with their corresponding HLA proteins using molecular docking and molecular dynamics simulation experiments, followed by binding free energy calculations with molecular mechanics Poisson-Boltzmann surface area, essential dynamics analysis and free energy landscape analysis. The immuno-informatics pipeline described and the candidate epitopes discovered herein could have significant impact upon efforts to develop globally effective SARS-CoV-2 vaccines

Multiplexed identification, quantification and genotyping of infectious agents using a semiconductor biochip

The emergence of pathogens resistant to existing antimicrobial drugs is a growing worldwide health crisis that threatens a return to the pre-antibiotic era. To decrease the overuse of antibiotics, molecular diagnostics systems are needed that can rapidly identify pathogens in a clinical sample and determine the presence of mutations that confer drug resistance at the point of care. We developed a fully integrated, miniaturized semiconductor biochip and closed-tube detection chemistry that performs multiplex nucleic acid amplification and sequence analysis. The approach had a high dynamic range of quantification of microbial load and was able to perform comprehensive mutation analysis on up to 1,000 sequences or strands simultaneously in <2 h. We detected and quantified multiple DNA and RNA respiratory viruses in clinical samples with complete concordance to a commercially available test. We also identified 54 drug-resistance-associated mutations that were present in six genes of Mycobacterium tuberculosis, all of which were confirmed by next-generation sequencing