Cox process representation and inference for stochastic reaction-diffusion processes

Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate to observational data. Here we use ideas from statistical physics and machine learning to provide a solution to the inverse problem of learning a stochastic reaction-diffusion process from data. Our solution relies on a non-trivial connection between stochastic reaction-diffusion processes and spatio-temporal Cox processes, a well-studied class of models from computational statistics. This connection leads to an efficient and flexible algorithm for parameter inference and model selection. Our approach shows excellent accuracy on numeric and real data examples from systems biology and epidemiology. Our work provides both insights into spatio-temporal stochastic systems, and a practical solution to a long-standing problem in computational modelling

Generation of flavors and fragrances through biotransformation and de novo synthesis

Flavors and fragrances are the result of the presence of volatile and non-volatile compounds, appreciated mostly by the sense of smell once they usually have pleasant odors. They are used in perfumes and perfumed products, as well as for the flavoring of foods and beverages. In fact the ability of the microorganisms to produce flavors and fragrances has been described for a long time, but the relationship between the flavor formation and the microbial growth was only recently established. After that, efforts have been put in the analysis and optimization of food fermentations that led to the investigation of microorganisms and their capacity to produce flavors and fragrances, either by de novo synthesis or biotransformation. In this review, we aim to resume the recent achievements in the production of the most relevant flavors by bioconversion/biotransformation or de novo synthesis, its market value, prominent strains used, and their production rates/maximum concentrations.We would like to thank the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469 unit, COMPETE 2020 (POCI-01-0145FEDER-006684), and BiotecNorte operation (NORTE-01-0145FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020—Programa Operacional Regional do Norte.info:eu-repo/semantics/publishedVersio

The SprayList: a scalable relaxed priority queue

High-performance concurrent priority queues are essential for applications such as task scheduling and discrete event simulation. Unfortunately, even the best performing implementations do not scale past a number of threads in the single digits. This is because of the sequential bottleneck in accessing the elements at the head of the queue in order to perform a DeleteMin operation. In this paper, we present the SprayList, a scalable priority queue with relaxed ordering semantics. Starting from a non-blocking SkipList, the main innovation behind our design is that the DeleteMin operations avoid a sequential bottleneck by "spraying'' themselves onto the head of the SkipList list in a coordinated fashion. The spraying is implemented using a carefully designed random walk, so that DeleteMin returns an element among the first O(p log[superscript 3] p) in the list, with high probability, where p is the number of threads. We prove that the running time of a DeleteMin operation is O(log[superscript 3] p), with high probability, independent of the size of the list. Our experiments show that the relaxed semantics allow the data structure to scale for high thread counts, comparable to a classic unordered SkipList. Furthermore, we observe that, for reasonably parallel workloads, the scalability benefits of relaxation considerably outweigh the additional work due to out-of-order execution.National Science Foundation (U.S.) (Grant CCF-1217921)National Science Foundation (U.S.) (Grant CCF-1301926)National Science Foundation (U.S.) (Grant IIS-1447786)United States. Dept. of Energy (Grant ER26116/DE-SC0008923)Oracle CorporationIntel Corporatio