AIMS/INTRODUCTION: Coronavirus disease 2019 (COVID-19) vaccinations have been proven to be generally safe in healthy populations. However, the data on vaccine safety in patients with type 1 diabetes are scarce. This study aimed to evaluate the frequency and severity of short-term (<7-day) adverse vaccination events (AEs) and their risk factors among type 1 diabetes patients.

MATERIALS AND METHODS: This study analyzed data from the COVID-19 vaccination in Autoimmune Diseases (COVAD) survey database (May to December 2021; 110 collaborators, 94 countries), comparing <7-day COVID-19 vaccine AE among type 1 diabetes patients and healthy controls (HCs). Descriptive statistics; propensity score matching (1:4) using the variables age, sex and ethnicity; and multivariate analyses were carried out.

RESULTS: This study analyzed 5,480 completed survey responses. Of all responses, 5,408 were HCs, 72 were type 1 diabetes patients (43 females, 48.0% white European ancestry) and Pfizer was the most administered vaccine (39%). A total of 4,052 (73.9%) respondents had received two vaccine doses. Patients with type 1 diabetes had a comparable risk of injection site pain, minor and major vaccine AEs, as well as associated hospitalizations to HCs. However, type 1 diabetes patients had a higher risk of severe rashes (3% vs 0.4%, OR 8.0, 95% confidence interval 1.7-36), P = 0.007), although reassuringly, these were rare (n = 2 among type 1 diabetes patients).

CONCLUSIONS: COVID-19 vaccination was safe and well tolerated in patients with type 1 diabetes with similar AE profiles compared with HCs, although severe rashes were more common in type 1 diabetes patients.

The intensified interactions between power and gas systems and the wide utilization of renewable energy introduce additional challenges in energy marketing and pricing mechanisms. Existing studies either neglect energy uncertainties in deterministic market-clearing models, use predetermined strategic offering prices in single-level models, or approximate gas dynamics in nonrealistic models. This paper proposes a bi-level two-stage distributionally robust electricity–gas market clearing (EG-MC) model considering energy uncertainties and strategic offering prices from energy producers. Strategic energy producers submit their offering prices in the upper-level problem to the EG-MC operator, who maximizes market profits under the realizations of renewable energy outputs while balancing the robustness and conservativeness of the day-ahead market decisions. The presence of gas dynamics in the two stages of the decision-making framework generates an intractable EG-MC problem. A novel triple-loop procedure, namely inner and outer columns & constraints generation and bilinear approximation algorithms, is proposed to sufficiently solve the formulated model. Finally, numerical analyses on an EG-MC model demonstrate the effectiveness of the distributionally robust strategic offers and the performances of the solution methodology.

The intensified interactions between power and gas systems and the wide utilization of renewable energy introduce additional challenges in energy marketing and pricing mechanisms. Existing studies either neglect energy uncertainties in deterministic market-clearing models, use predetermined strategic offering prices in single-level models, or approximate gas dynamics in nonrealistic models. This paper proposes a bi-level two-stage distributionally robust electricity–gas market clearing (EG-MC) model considering energy uncertainties and strategic offering prices from energy producers. Strategic energy producers submit their offering prices in the upper-level problem to the EG-MC operator, who maximizes market profits under the realizations of renewable energy outputs while balancing the robustness and conservativeness of the day-ahead market decisions. The presence of gas dynamics in the two stages of the decision-making framework generates an intractable EG-MC problem. A novel triple-loop procedure, namely inner and outer columns & constraints generation and bilinear approximation algorithms, is proposed to sufficiently solve the formulated model. Finally, numerical analyses on an EG-MC model demonstrate the effectiveness of the distributionally robust strategic offers and the performances of the solution methodology.

The location of major quantitative trait loci (QTL) contributing to stem and leaf [Na+] and [K+] was previously reported in chromosome 7 using two connected populations of recombinant inbred lines (RILs) of tomato. HKT1;1 and HKT1;2, two tomato Na+-selective class I-HKT transporters, were found to be closely linked, where the maximum logarithm of odds (LOD) score for these QTLs located. When a chromosome 7 linkage map based on 278 single-nucleotide polymorphisms (SNPs) was used, the maximum LOD score position was only 35kb from HKT1;1 and HKT1;2. Their expression patterns and phenotypic effects were further investigated in two near-isogenic lines (NILs): 157-14 (double homozygote for the cheesmaniae alleles) and 157-17 (double homozygote for the lycopersicum alleles). The expression pattern for the HKT1;1 and HKT1;2 alleles was complex, possibly because of differences in their promoter sequences. High salinity had very little effect on root dry and fresh weight and consequently on the plant dry weight of NIL 157-14 in comparison with 157-17. A significant difference between NILs was also found for [K+] and the [Na+]/[K+] ratio in leaf and stem but not for [Na+] arising a disagreement with the corresponding RIL population. Their association with leaf [Na+] and salt tolerance in tomato is also discussed. © 2012 Blackwell Publishing Ltd.

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Rectal endometriosis is a very indolent disease whose treatment has been debated by a range of competing schools. Meanwhile, not all audiences in the scientific community are entirely familiar with the full aspects of the disease. Hence, the purpose of this review is to outline the basic as well as the recent literature pertaining to the disease, thus offering a broader view to the interested reader.