Clustering method with axiomatization to support failure mode and effect analysis

Failure Mode and Effect Analysis (FMEA) is a highly structured risk-prevention management process that improves the reliability and safety of a system. This article investigates one of the most critical issues in FMEA practice: Clustering failure modes based on their risks. In the failure mode clustering problem, all identified failure modes need to be assigned to several predefined and risk-ordered categories to manage their risks. We model the clustering of failure modes through multi-expert multiple criteria decision making with an additive value function, and call it the additive N -clustering problem. We begin by proposing six axioms that describe an ideal clustering method in the additive N -clustering problem, and find that the EXogenous Clustering Method (EXCM), where category thresholds can be exogenously provided, is ideal (Exogenous Possibility Theorem), whereas any endogenous clustering method, where the clustering is determined endogenously in the given method, cannot satisfy all six axioms simultaneously (Endogenous Impossibility Theorem). In practice, endogenous clustering methods are important, due to the difficulty in providing accurate and reasonable category thresholds of the EXCM. Therefore, we propose the Consensus-based ENdogenous Clustering Method (CENCM) and discuss its axiomatic properties. We also apply the CENCM to the SARS-CoV-2 prevention case and justify the CENCM through axiomatic comparisons and a detailed simulation experiment. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Selective degradation of tRNASer(AGY) is the primary driver for mitochondrial seryl-tRNA synthetase-related disease.

Mitochondrial translation is of high significance for cellular energy homeostasis. Aminoacyl-tRNA synthetases (aaRSs) are crucial translational components. Mitochondrial aaRS variants cause various human diseases. However, the pathogenesis of the vast majority of these diseases remains unknown. Here, we identified two novel SARS2 (encoding mitochondrial seryl-tRNA synthetase) variants that cause a multisystem disorder. c.654-14T > A mutation induced mRNA mis-splicing, generating a peptide insertion in the active site; c.1519dupC swapped a critical tRNA-binding motif in the C-terminus due to stop codon readthrough. Both mutants exhibited severely diminished tRNA binding and aminoacylation capacities. A marked reduction in mitochondrial tRNASer(AGY) was observed due to RNA degradation in patient-derived induced pluripotent stem cells (iPSCs), causing impaired translation and comprehensive mitochondrial function deficiencies. These impairments were efficiently rescued by wild-type SARS2 overexpression. Either mutation caused early embryonic fatality in mice. Heterozygous mice displayed reduced muscle tissue-specific levels of tRNASers. Our findings elucidated the biochemical and cellular consequences of impaired translation mediated by SARS2, suggesting that reduced abundance of tRNASer(AGY) is a key determinant for development of SARS2-related diseases.

Clustering method with axiomatization to support failure mode and effect analysis

Failure mode and effect analysis (FMEA) is a highly structured risk-prevention management process that improves the reliability and safety of a system. This paper investigates one of the most critical issues in FMEA practice: Clustering failure modes based on their risks. In the failure mode clustering problem, all identified failure modes need to be assigned to several predefined and risk-ordered categories to manage their risks. We model the failure mode clustering through multi-expert multiple criteria decision making with an additive value function and call it the additive N -clustering problem. We begin by proposing six axioms that describe an ideal clustering method in the additive N -clustering problem, and find that the exogenous clustering method (EXCM), where category thresholds can be exogenously provided, is ideal (Exogenous Possibility Theorem), while any endogenous clustering method, where the clustering is determined endogenously in the given method, cannot satisfy all six axioms simultaneously (Endogenous Impossibility Theorem). In practice, endogenous clustering methods are important because of the difficulty in providing accurate and reasonable category thresholds of the EXCM. Therefore, we propose the consensus-based endogenous clustering method (CENCM) and discuss its axiomatic properties. We also apply the CENCM to the SARS-CoV-2 prevention case and justify the CENCM through axiomatic comparisons and a detailed simulation experiment. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Assessment of Retrospective COVID-19 Spatial Clusters with Respect to Demographic Factors: Case Study of Kansas City, Missouri, United States.

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The United States (U.S.) has the highest number of reported COVID-19 infections and related deaths in the world, accounting for 17.8% of total global confirmed cases as of August 2021. As COVID-19 spread throughout communities across the U.S., it became clear that inequities would arise among differing demographics. Several researchers have suggested that certain racial and ethnic minority groups may have been disproportionately impacted by the spread of COVID-19. In the present study, we used the daily data of COVID-19 cases in Kansas City, Missouri, to observe differences in COVID-19 clusters with respect to gender, race, and ethnicity. Specifically, we utilized a retrospective Poisson spatial scan statistic with respect to demographic factors to detect daily clusters of COVID-19 in Kansas City at the zip code level from March to November 2020. Our statistical results indicated that clusters of the male population were more widely scattered than clusters of the female population. Clusters of the Hispanic population had the highest prevalence and were also more widely scattered. This demographic cluster analysis can provide guidance for reducing the social inequalities associated with the COVID-19 pandemic. Moreover, applying stronger preventive and control measures to emerging clusters can reduce the likelihood of another epidemic wave of infection.

Short-term Effects of Canceled Elective Procedures Due to COVID-19: Evidence From the Veterans Affairs Healthcare System.

OBJECTIVE: To determine whether delayed or canceled elective procedures due to COVID-19 resulted in higher rates of ED utilization and/or increased mortality. SUMMARY OF BACKGROUND DATA: On March 15, 2020, the VA issued a nationwide order to temporarily pause elective cases due to COVID-19. The effects of this disruption on patient outcomes are not yet known. METHODS: This retrospective cohort study used data from the VA Corporate Data Warehouse. Surgical procedures canceled due to COVID-19 in 2020 (n = 3326) were matched to similar completed procedures in 2018 (n = 151,863) and 2019 (n = 146,582). Outcome measures included 30- and 90-day VA ED use and mortality in the period following the completed or canceled procedure. We used exact matching on surgical procedure category and nearest neighbor matching on patient characteristics, procedure year, and facility. RESULTS: Patients with elective surgical procedures canceled due to COVID-19 were no more likely to have an ED visit in the 30- [Difference: -4.3% pts; 95% confidence interval (CI): -0.078, -0.007] and 90 days (-0.9% pts; 95% CI: -0.068, 0.05) following the expected case date. Patients with cancellations had no difference in 30- (Difference: 0.1% pts; 95% CI: -0.008, 0.01) and 90-day (Difference: -0.4% pts; 95% CI: -0.016, 0.009) mortality rates when compared to similar patients with similar procedures that were completed in previous years. CONCLUSIONS: The pause in elective surgical cases was not associated with short-term adverse outcomes in VA hospitals, suggesting appropriate surgical case triage and management. Further study will be essential to determine if the delayed cases were associated with longer-term effects.

Effects of SARS-CoV-2 mutations on protein structures and intraviral protein-protein interactions.

Since 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) has infected 10 millions of people across the globe, and massive mutations in virus genome have occurred during the rapid spread of this novel coronavirus. Variance in protein sequence might lead to a change in protein structure and interaction, then further affect the viral physiological characteristics, which could bring tremendous influence on the pandemic. In this study, we investigated 20 nonsynonymous mutations in the SARS-CoV-2 genome in which incidence rates were all ≥ 1% as of September 1st, 2020, and then modeled and analyzed the mutant protein structures. The results showed that four types of mutations caused dramatic changes in protein structures (RMSD ≥ 5.0 Å), which were Q57H and G251V in open-reading frames 3a (ORF3a), S194L, and R203K/G204R in nucleocapsid (N). Next, we found that these mutations also affected the binding affinity of intraviral protein interactions. In addition, the hot spots within these docking mutant complexes were altered, among which the mutation Q57H was involved in both Orf3a-S and Orf3a-Orf8 protein interactions. Besides, these mutations were widely distributed all over the world, and their occurrences fluctuated as time went on. Notably, the incidences of R203K/G204R in N and Q57H in Orf3a were both over 50% in some countries. Overall, our findings suggest that SARS-CoV-2 mutations could change viral protein structure, binding affinity, and hot spots of the interface, thereby might have impacts on SARS-CoV-2 transmission, diagnosis, and treatment of COVID-19.