This study is registered under the identifier NCT04832841 on ClinicalTrials.gov.

Background and Aim: Plant-based diets are associated with potential health benefits, but the contribution of gut microbiota remains to be clarified. We aimed to identify differences in key features of microbiome composition and function with relevance to metabolic health in individuals adhering to a vegan vs. omnivore diet.Methods: This cross-sectional study involved lean, healthy vegans (n = 62) and omnivore (n = 33) subjects. We assessed their glucose and lipid metabolism and employed an integrated multi-omics approach (16S rRNA sequencing, metabolomics profiling) to compare dietary intake, metabolic health, gut microbiome, and fecal, serum, and urine metabolomes.Results: The vegans had more favorable glucose and lipid homeostasis profiles than the omnivores. Long-term reported adherence to a vegan diet affected only 14.8% of all detected bacterial genera in fecal microbiome. However, significant differences in vegan and omnivore metabolomes were observed. In feces, 43.3% of all identified metabolites were significantly different between the vegans and omnivores, such as amino acid fermentation products p-cresol, scatole, indole, methional (lower in the vegans), and polysaccharide fermentation product short- and medium-chain fatty acids (SCFAs, MCFAs), and their derivatives (higher in the vegans). Vegan serum metabolome differed markedly from the omnivores (55.8% of all metabolites), especially in amino acid composition, such as low BCAAs, high SCFAs (formic-, acetic-, propionic-, butyric acids), and dimethylsulfone, the latter two being potential host microbiome co-metabolites. Using a machine-learning approach, we tested the discriminative power of each dataset. Best results were obtained for serum metabolome (accuracy rate 91.6%).Conclusion: While only small differences in the gut microbiota were found between the groups, their metabolic activity differed substantially. In particular, we observed a significantly different abundance of fermentation products associated with protein and carbohydrate intakes in the vegans. Vegans had significantly lower abundances of potentially harmful (such as p-cresol, lithocholic acid, BCAAs, aromatic compounds, etc.) and higher occurrence of potentially beneficial metabolites (SCFAs and their derivatives).

In this work, we consider a scheduling problem faced by production companies with large electricity consumption. Due to the contract with the electric utility, the production companies are obligated to comply with the total energy consumption limits in the specified time intervals (usually 15-minutes long); otherwise, the companies pay substantial penalty fees. Although it is possible to design production schedules that consider these limits as hard constraints, uncertainties occurring during the execution of the schedules are usually not taken into account. This may lead to situations in which the unexpected delays of the operations cause the violations of the energy consumption limits. Our goal is to design robust production schedules pro-actively guaranteeing that the energy consumption limits are not violated for the given set of uncertainty scenarios. We consider scheduling on one machine with release times of the operations and total tardiness as the objective function.To tackle this problem, we first propose a pseudo-polynomial algorithm for finding the optimal robust schedule for the given permutation of the operations. This algorithm is then utilised in three different algorithms for finding the optimal permutation: two exact (Branch-and-Bound and logic-based Benders decomposition) and one heuristic algorithm (tabu search). All the algorithms were experimentally evaluated on random instances with different sizes of the uncertainty scenarios set. Using the tabu search algorithm, we are able to solve large instances within one minute.

The authors compared the rate of SARS-CoV-2 infection in kidney transplant recipients among those who did and did not receive a messenger RNA vaccine in the Czech Republic between February and May 2021 while the Alpha variant was predominant.

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In this paper, we investigate the use of deep learning for solving a classical N P -hard single machine scheduling problem where the criterion is to minimize the total tardiness. Instead of designing an end-to-end machine learning model, we utilize well known decomposition of the problem and we enhance it with a data-driven approach. We have designed a regressor containing a deep neural network that learns and predicts the criterion of a given set of jobs. The network acts as a polynomial-time estimator of the criterion that is used in a singlepass scheduling algorithm based on Lawler's decomposition theorem. Essentially, the regressor guides the algorithm to select the best position for each job. The experimental results show that our data-driven approach can efficiently generalize information from the training phase to significantly larger instances (up to 350 jobs) where it achieves an optimality gap of about 0.5%, which is four times less than the gap of the state-of-the-art NBR heuristic. d https://orcid.org/0000-0003-4692-1625 e https://orcid.org/0000-0002-8135-1296 jobs, which is significantly more than, e.g., an end-toend approach (Vinyals et al., 2015) that solves Traveling Salesman Problem with about 50 nodes. Our proposed approach outperforms the state-of-the-art heuristic for SMTTP.

Butyrate is formed in the gut during bacterial fermentation of dietary fiber and is attributed numerous beneficial effects on the host metabolism. We aimed to develop a method for the assessment of functional capacity of gut microbiota butyrate synthesis based on the qPCR quantification of bacterial gene coding butyryl-CoA:acetate CoA-transferase, the key enzyme of butyrate synthesis. In silico, we identified bacteria possessing but gene among human gut microbiota by searching but coding sequences in available databases. We designed and validated six sets of degenerate primers covering all selected bacteria, based on their phylogenetic nearness and sequence similarity, and developed a method for gene abundance normalization in human fecal DNA. We determined but gene abundance in fecal DNA of subjects with opposing dietary patterns and metabolic phenotypes—lean vegans (VG) and healthy obese omnivores (OB) with known fecal microbiota and metabolome composition. We found higher but gene copy number in VG compared with OB, in line with higher fecal butyrate content in VG group. We further found a positive correlation between the relative abundance of target bacterial genera identified by next-generation sequencing and groups of but genecontaining bacteria determined by specific primers. In conclusion, this approach represents a simple and feasible tool for estimation of microbial functional capacity.

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