Sustainable supplier selection and order allocation for multinational enterprises considering supply disruption in COVID-19 era

The unprecedented outbreak of COVID-19 has left many multinational enterprises facing extremely severe supply disruptions. Besides considering triple-bottom-line requirements, managers now also have to consider supply disruption due to the pandemic more seriously. However, existing research does not take these two key objectives into account simultaneously. To bridge this research gap, based on the characteristics of COVID-19 and similar global emergency events, this article proposes a model that aims to solve the problem of sustainable supplier selection and order allocation considering supply disruption in the COVID-19 era. It does so by using a multi-stage multi-objective optimization model applied to the different stages of development and spread of the pandemic. Then, a novel nRa-NSGA-II algorithm is proposed to solve the high-dimensional multi-objective optimization model. The applicability and effectiveness of the proposed model is illustrated in a well-known multinational producer of shortwave therapeutic instruments.JEL Classification: M11

SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification.

Genes with moderate to low expression heritability may explain a large proportion of complex trait etiology, but such genes cannot be sufficiently captured in conventional transcriptome-wide association studies (TWASs), partly due to the relatively small available reference datasets for developing expression genetic prediction models to capture the moderate to low genetically regulated components of gene expression. Here, we introduce a method, the Summary-level Unified Method for Modeling Integrated Transcriptome (SUMMIT), to improve the expression prediction model accuracy and the power of TWAS by using a large expression quantitative trait loci (eQTL) summary-level dataset. We apply SUMMIT to the eQTL summary-level data provided by the eQTLGen consortium. Through simulation studies and analyses of genome-wide association study summary statistics for 24 complex traits, we show that SUMMIT improves the accuracy of expression prediction in blood, successfully builds expression prediction models for genes with low expression heritability, and achieves higher statistical power than several benchmark methods. Finally, we conduct a case study of COVID-19 severity with SUMMIT and identify 11 likely causal genes associated with COVID-19 severity.

Sustainable supplier selection and order allocation for multinational enterprises considering supply disruption in COVID-19 era

The unprecedented outbreak of COVID-19 has left many multinational enterprises facing extremely severe supply disruptions. Besides considering triple-bottom-line requirements, managers now also have to consider supply disruption due to the pandemic more seriously. However, existing research does not take these two key objectives into account simultaneously. To bridge this research gap, based on the characteristics of COVID-19 and similar global emergency events, this article proposes a model that aims to solve the problem of sustainable supplier selection and order allocation considering supply disruption in the COVID-19 era. It does so by using a multi-stage multi-objective optimization model applied to the different stages of development and spread of the pandemic. Then, a novel nRa-NSGA-II algorithm is proposed to solve the high-dimensional multi-objective optimization model. The applicability and effectiveness of the proposed model is illustrated in a well-known multinational producer of shortwave therapeutic instruments.JEL Classification: M11

An integrative multiomics analysis identifies putative causal genes for COVID-19 severity.

PURPOSE: It is critical to identify putative causal targets for SARS coronavirus 2, which may guide drug repurposing options to reduce the public health burden of COVID-19. METHODS: We applied complementary methods and multiphased design to pinpoint the most likely causal genes for COVID-19 severity. First, we applied cross-methylome omnibus (CMO) test and leveraged data from the COVID-19 Host Genetics Initiative (HGI) comparing 9,986 hospitalized COVID-19 patients and 1,877,672 population controls. Second, we evaluated associations using the complementary S-PrediXcan method and leveraging blood and lung tissue gene expression prediction models. Third, we assessed associations of the identified genes with another COVID-19 phenotype, comparing very severe respiratory confirmed COVID versus population controls. Finally, we applied a fine-mapping method, fine-mapping of gene sets (FOGS), to prioritize putative causal genes. RESULTS: Through analyses of the COVID-19 HGI using complementary CMO and S-PrediXcan methods along with fine-mapping, XCR1, CCR2, SACM1L, OAS3, NSF, WNT3, NAPSA, and IFNAR2 are identified as putative causal genes for COVID-19 severity. CONCLUSION: We identified eight genes at five genomic loci as putative causal genes for COVID-19 severity.

Early cardiac involvement in patients with acute COVID-19 infection identified by multiparametric cardiovascular magnetic resonance imaging.

AIMS: In order to determine acute cardiac involvement in patients with COVID-19, we quantitatively evaluated tissue characteristics and mechanics by non-invasive cardiac magnetic resonance (CMR) in a cohort of patients within the first 10 days of the onset of COVID symptoms. METHODS AND RESULTS: Twenty-five patients with reverse transcription polymerase chain reaction confirmed COVID-19 and at least one marker of cardiac involvement [cardiac symptoms, abnormal electrocardiograph (ECG), or abnormal cardiac biomarkers] and 25 healthy age- and gender-matched control subjects were recruited to the study. Patients were divided into those with elevated (n = 8) or normal TnI (n = 17). There were significant differences in global longitudinal strain among patients who were positive and negative for hs-TnI, and controls [-12.3 (-13.3, -11.5)%, -13.1 (-14.2, -9.8)%, and -15.7 (-18.3, -12.7)%, P = 0.004]. Native myocardial T1 relaxation times in patients with positive and negative hs-TnI manifestation (1169.8 ± 12.9 and 1113.2 ± 31.2 ms) were significantly higher than the normal (1065 ± 57 ms) subjects, respectively (P < 0.001). The extracellular volume (ECV) of patients who were positive and negative for hs-TnI was higher than that of the normal controls [32 (31, 33)%, 29 (27, 30)%, and 26 (24, 27.5)%, P < 0.001]. In our study, quantitative T2 mapping in patients who were positive and negative for hs-TnI [51 (47.9, 52.8) and 48 (47, 49.4) ms] was significantly higher than the normal [42 (41, 45.2) ms] subjects (P < 0.001). CONCLUSION: In patients with early-stage COVID-19, myocardial oedema, and functional abnormalities are a frequent finding, while irreversible regional injury such as necrosis may be infrequent.

Associations Between Genetically Predicted Protein Levels and COVID-19 Severity.

It is critical to identify potential causal targets for SARS-CoV-2, which may guide drug repurposing options. We assessed the associations between genetically predicted protein levels and COVID-19 severity. Leveraging data from the COVID-19 Host Genetics Initiative comparing 6492 hospitalized COVID-19 patients and 1 012 809 controls, we identified 18 proteins with genetically predicted levels to be associated with COVID-19 severity at a false discovery rate of <0.05, including 12 that showed an association even after Bonferroni correction. Of the 18 proteins, 6 showed positive associations and 12 showed inverse associations. In conclusion, we identified 18 candidate proteins for COVID-19 severity.