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Identification and elimination of false positives in electrochemical nitrogen reduction studies.

ABSTRACT: Ammonia is of emerging interest as a liquefied, renewable-energy-sourced energy carrier for global use in the future. Electrochemical reduction of N₂ (NRR) is widely recognised as an alternative to the traditional Haber-Bosch production process for ammonia. However, though the challenges of NRR experiments have become better understood, the reported rates are often too low to be convincing that reduction of the highly unreactive N₂ molecule has actually been achieved. This perspective critically reassesses a wide range of the NRR reports, describes experimental case studies of potential origins of false-positives, and presents an updated, simplified experimental protocol dealing with the recently emerging issues.

SUBMITTER: Choi J

PROVIDER: S-EPMC7641139 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Identification and elimination of false positives in electrochemical nitrogen reduction studies.

Choi Jaecheol J Suryanto Bryan H R BHR Wang Dabin D Du Hoang-Long HL Hodgetts Rebecca Y RY Ferrero Vallana Federico M FM MacFarlane Douglas R DR Simonov Alexandr N AN

Nature communications 20201103 1

Ammonia is of emerging interest as a liquefied, renewable-energy-sourced energy carrier for global use in the future. Electrochemical reduction of N<sub>2</sub> (NRR) is widely recognised as an alternative to the traditional Haber-Bosch production process for ammonia. However, though the challenges of NRR experiments have become better understood, the reported rates are often too low to be convincing that reduction of the highly unreactive N<sub>2</sub> molecule has actually been achieved. This ...[more]

PMID: 33144566

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Project description:BackgroundA large number of probabilistic models used in sequence analysis assign non-zero probability values to most input sequences. To decide when a given probability is sufficient the most common way is bayesian binary classification, where the probability of the model characterizing the sequence family of interest is compared to that of an alternative probability model. We can use as alternative model a null model. This is the scoring technique used by sequence analysis tools such as HMMER, SAM and INFERNAL. The most prevalent null models are position-independent residue distributions that include: the uniform distribution, genomic distribution, family-specific distribution and the target sequence distribution. This paper presents a study to evaluate the impact of the choice of a null model in the final result of classifications. In particular, we are interested in minimizing the number of false predictions in a classification. This is a crucial issue to reduce costs of biological validation.ResultsFor all the tests, the target null model presented the lowest number of false positives, when using random sequences as a test. The study was performed in DNA sequences using GC content as the measure of content bias, but the results should be valid also for protein sequences. To broaden the application of the results, the study was performed using randomly generated sequences. Previous studies were performed on aminoacid sequences, using only one probabilistic model (HMM) and on a specific benchmark, and lack more general conclusions about the performance of null models. Finally, a benchmark test with P. falciparum confirmed these results.ConclusionsOf the evaluated models the best suited for classification are the uniform model and the target model. However, the use of the uniform model presents a GC bias that can cause more false positives for candidate sequences with extreme compositional bias, a characteristic not described in previous studies. In these cases the target model is more dependable for biological validation due to its higher specificity.

Dataset Information

Identification and elimination of false positives in electrochemical nitrogen reduction studies.

Publications

Identification and elimination of false positives in electrochemical nitrogen reduction studies.

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