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Iacovelli, R.; He, T.; Allen, J. L.; Hackl, T.; Haslinger, K.(
, Fungal Biology and Biotechnology)
AbstractBackground
Filamentous fungi are prolific producers of bioactive molecules and enzymes with important applications in industry. Yet, the vast majority of fungal species remain undiscovered or uncharacterized. Here we focus our attention to a wild fungal isolate that we identified asAnthostomella pinea. The fungus belongs to a complex polyphyletic genus in the family ofXylariaceae, which is known to comprise endophytic and pathogenic fungi that produce a plethora of interesting secondary metabolites. Despite that,Anthostomellais largely understudied and only two species have been fully sequenced and characterized at a genomic level.
Results
In this work, we used long-read sequencing to obtain the complete 53.7 Mb genome sequence including the full mitochondrial DNA. We performed extensive structural and functional annotation of coding sequences, including genes encoding enzymes with potential applications in biotechnology. Among others, we found that the genome ofA. pineaencodes 91 biosynthetic gene clusters, more than 600 CAZymes, and 164 P450s. Furthermore, untargeted metabolomics and molecular networking analysis of the cultivation extracts revealed a rich secondary metabolism, and in particular an abundance of sesquiterpenoids and sesquiterpene lactones. We also identified the polyketide antibiotic xanthoepocin, to which we attribute the anti–Gram-positive effect of the extracts that we observed in antibacterial plate assays.
Conclusions
Taken together, our results provide a first glimpse into the potential ofAnthstomella pineato provide new bioactive molecules and biocatalysts and will facilitate future research into these valuable metabolites.
An integrated urban transportation system usually
consists of multiple transport modes that have complementary
characteristics of capacities, speeds, and costs, facilitating smooth
passenger transfers according to planned schedules. However,
such an integration is not designed to operate under disruptive
events, e.g., a signal failure at a subway station or a breakdown
of a bus, which have rippling effects on passenger demand
and significantly increase delays. To address these disruptive
events, current solutions mainly rely on a substitute service
to transport passengers from and to affected areas using adhoc
schedules. To fully utilize heterogeneous
transportation systems under disruptive events, we design
a service called eRoute based on a hierarchical receding horizon
control framework to automatically reroute, reschedule, and
reallocate multi-mode transportation systems based on real-time
and predicted demand and supply. Focusing on an integration of
subway and bus, we implement and evaluate eRoute with large
datasets including (i) a bus system with 13,000 buses, (ii) a subway
system with 127 subway stations, (iii) an automatic fare collection
system with a total of 16,840 readers and 8 million card users
from a metropolitan city. The data-driven evaluation results show
that our solution improves the ratio of served passengers (RSP)
by up to 11.5 times and reduces the average traveling time by
up to 82.1% compared with existing solutions.
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