A method for independent estimation of false localisation rate for phosphoproteomics

Phosphoproteomics methods are commonly employed in labs to identify and quantify the sites of phosphorylation on proteins. In recent years, various software tools have been developed, incorporating scores or statistics related to whether a given phosphosite has been correctly identified, or to estimate the global false localisation rate (FLR) within a given data set for all sites reported. These scores have generally been calibrated using synthetic data sets, and their statistical reliability on real datasets is largely unknown. As a result, there is considerable problem in the field of reporting incorrectly localised phosphosites, due to inadequate statistical control. In this work, we develop the concept of using scoring and ranking modifications on a decoy amino acid, i.e. one that cannot be modified, to allow for independent estimation of global FLR. We test a variety of different amino acids to act as the decoy, on both synthetic and real data sets, demonstrating that the amino acid selection can make a substantial difference to the estimated global FLR. We conclude that while several different amino acids might be appropriate, the most reliable FLR results were achieved using alanine and leucine as decoys, although we have a preference for alanine due to the risk of potential confusion between leucine and isoleucine amino acids. We propose that the phosphoproteomics field should adopt the use of a decoy amino acid, so that there is better control of false reporting in the literature, and in public databases that re-distribute the data.

TFCrowd: a blockchain-based crowdsourcing framework with enhanced trustworthiness and fairness

Blockchain technology has attracted considerable attention due to the boom of cryptocurrencies and decentralized applications. Among them, the emerging blockchain-based crowdsourcing is a typical paradigm, which gets rid of centralized cloud-servers and leverages smart contracts to realize task recommendation and reward distribution. However, there are still two critical issues yet to be solved urgently. First, malicious evaluation from crowdsourcing requesters will result in honest workers not getting the rewards they deserve even if they have provided valuable solutions. Second, unfair evaluation and reward distribution can lead to low enthusiasm for work. Therefore, the above problems will seriously hinder the development of blockchain-based crowdsourcing platforms. In this paper, we propose a new blockchain-based crowdsourcing framework with enhanced trustworthiness and fairness, named TFCrowd. The core idea of TFCrowd is utilizing a smart contract of blockchain as a trusted authority to fairly evaluate contributions and allocate rewards. To this end, we devise a reputation-based evaluation mechanism to punish the requester who behaves as “false-reporting” and a Shapley value-based method to distribute rewards fairly. By using our proposed schemes, TFCrowd can prevent malicious requesters from making unfair comments and reward honest workers according to their contributions. Extensive simulations and the experiment results demonstrate that TFCrowd can protect the interests of workers and distribute rewards fairly.

Understandable public anger

In the ten years from 2008 onwards the banking sector was constantly in the spotlight. Blame for the financial crisis and concern regarding controversial government bailouts were followed by public outrage about inflated bonuses, money laundering and false reporting. Over this period, banks deployed a range of legitimation strategies to salvage their reputation. This paper proposes a modified typology of legitimation strategies based on previous research (van Leeuwen and Wodak 1999; Vaara, Tienari and Laurila 2006), and examines how these are used by in the “letter to shareholders” published by the chairs of the five main UK-based banks over the ten years following the crisis. The strategies are analysed in terms of their object, target and interdiscursive features, and the particular persuasive roles of narrative and emotion are underlined.

Whistleblower rewards, false reports, and corporate fraud

It is often claimed that rewards for whistleblowers lead to fraudulent reports, but for several US programs this has not been a major problem. We model the interaction between rewards for whistleblowers, sanctions against fraudulent reporting, judicial errors, and standards of proof in the court case on a whistleblower’s allegations and the possible follow-up for fraudulent allegations. Balancing whistleblower rewards, sanctions against fraudulent reports, and courts’ standards of proof is essential for these policies to succeed. When the risk of retaliation is severe, larger rewards are needed and so are tougher sanctions against fraudulent reports. The precision of the legal system must be sufficiently high, hence these programs are not viable in weak institution environments, where protection is imperfect and court precision low, or where sanctions against false reporting are mild.

TFCrowd: A Blockchain-based Crowdsourcing Framework with Enhanced Trustworthiness and Fairness

Blockchain technology has attracted considerable attention due to the boom of cryptocurrencies and decentralized applications. Among them, the emerging blockchain-based crowdsourcing is a typical paradigm, which gets rid of centralized cloud-servers and leverages smart contracts to realize task recommendation and reward distribution. However, there are still two critical issues yet to be solved urgently. First, malicious evaluation from crowdsourcing requesters will result in honest workers not getting the rewards they deserve even providing valuable solutions. Second, unfair evaluation and reward distribution can lead to workers’ reluctance to work actively. Therefore, the above problems will seriously hinder the development of blockchain-based crowdsourcing platforms. In this paper, we propose a new blockchain-based crowdsourcing framework with enhanced trustworthiness and fairness, named TFCrowd. The core idea of TFCrowd is utilizing a smart contract of blockchain as a trusted authority to fairly evaluate contributions and allocate rewards. To this end, we devise a reputation-based evaluation mechanism to punish the requester who behaves as “false-reporting” and a Shapley value-based method to distribute rewards fairly. By using our proposed schemes, TFCrowd can prevent malicious requesters from making unfair comments and reward honest workers according to their contributions. Extensive simulations and the experiment results demonstrate that TFCrowd can protect the interests of workers and distribute rewards fairly.

Apathy and Ignorance: False Prophets of Autism and the Anti-Vaccine Movement

This chapter highlights the story of autism, the widespread acceptance of its incorrect cause, and the impact on use of vaccines, all stemming directly from deliberate, false reporting. The basic conflict is twofold. First, involvement of a scientific method that must be reproducible, be reliable, and possess substantial proof is in conflict with common/personal beliefs. Second, doctors, scientists, and public health workers, despite their mandate to listen to parents and patients concerning their opinions, must base medical conclusions on evidence that validates the outcome of each patient’s health issue. It is in this milieu that autism and the anti-vaccine groups still do battle. In 1998, Lancet, a usually respectable and reputable English journal, published Dr. Andrew Wakefield’s opinion that the measles, mumps, rubella (German measles) vaccine injected into the arms of children caused inflammation, leading to harmful chemicals entering the bloodstream through the gut (intestine). These factors, he said, traveled to the brain, where the harmful chemicals/toxins caused autism. In the face of this “fake news” about the source of autism and measles, the vaccination rate for measles dropped in the United Kingdom and Ireland.

A comparative study of active and passive adverse drug reaction reporting systems in terms of false reporting rate

Background: World Health Organisation Uppsala Monitoring Centre (WHO-UMC) was set up in 1968 to collect Adverse Drug Reactions (ADRs) periodically for all drugs across the globe. It identifies two main approaches to pharmacovigilance: active (intensive) and passive (spontaneous). However, very few studies are available to compare these two methods of adverse drug reaction reporting. Methods: A prospective observational study was done on 303 newly diagnosed patients with tuberculosis receiving directly observed therapy short-course (DOTS) in the Sawai Man Singh (SMS) Hospital, Jaipur between 1 January 2019 and 31 December 2019. They were randomly divided into groups A (150 patients) and B (153 patients). Group A patients were followed actively at fixed intervals of time for ADRs till next six months through electronic conversation or personal interview. Group B patients were required to report spontaneously for any ADRs to pharmacovigilance centre. After data collection causality assessment was done using the WHO-UMC scale to identify false reporting and finally results were analysed statistically by means of the t-test using Minitab 14 software Pennsylvania, USA. Results: 153 ADRs were reported in active and 39 in passive group. Hence the yield of ADR was four times more in active method. After causality assessment, 31 in group A and 12 in group B were found to be falsely related (unlikely) to antitubercular drugs. Two sample t-test revealed active method reported more false ADR (p = 0.005). Conclusion: Although active method of surveillance identifies more ADRs than passive method, it is also more prone to false reporting. Hence its benefits should be weighed against its cost before adopting it for countries with limited resources.