Individuals often experience ailments such as allergies, asthma and respiratory tract infections throughout the year. Weather reports often include estimations of common allergens that can affect these individuals. To describe the local ‘atmospheric microbiome’ in Lubbock, Texas, USA, we examined the culturable fungal and bacterial microbiome present in the air on calm and dust storm days using internal transcribed spacer (ITS)-1 and 16S rRNA amplicon sequencing, respectively. While some types of airborne fungi were frequently present throughout the year, distinct differences were also observed between calm and dust storm days. We also observed the influence of the origin of air parcels and wind elevation of the air trajectory. The most abundant genera of fungi identified during the study period were Cryptococcus, Aureobasidium, Alternaria, Cladosporium and Filobasidium. This observation was not surprising considering the agricultural intensive environment of West Texas. Interestingly, Cladosporium, a common allergenic mold, was increased during days with dust storm events. The predominant bacterial genera observed were Bacillus, Pseudomonas, Psychrobacter, Massilia and Exiguobacterium. The relative abundance of the psychrophiles, Psychrobacter and Exiguobacterium, was surprising, given the semi-aridity of West Texas. Coupling our observations with back trajectories of the wind (Hybrid Single-Particle Lagrangian Integrated Trajectory models) demonstrated that dust storms, regional anthropogenic activity and origin of air parcels are important influences on the diversity and temporal presence of the atmospheric microbiome.

No abstract

Data-adaptable embedded systems operate on a variety of data streams, which requires a large degree of configurability and adaptability to support runtime changes in data stream inputs. Data-adaptable reconfigurable embedded systems, when decomposed into a series of tasks, enable a flexible runtime implementation in which a system can transition the execution of certain tasks between hardware and software while simultaneously continuing to process data during the transition. Efficient runtime scheduling of task transitions is needed to optimize system throughput and latency of the reconfiguration and transition periods. In this article, we provide an overview of a runtime framework enabling the efficient transition of tasks between software and hardware in response to changes in system inputs. We further present and analyze several runtime transition scheduling algorithms and highlight the latency and throughput tradeoffs for two data-adaptable systems. To evaluate the task transition selection algorithms, a case study was performed on an adaptable JPEG2000 implementation as well as three other synchronous dataflow systems characterized by transition latency and communication load.