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1. Set it and forget it: utility-based scheduling for public displays

   https://doi.org/10.1007/s00779-020-01423-1  

   Bushman, Kristi ; Labrinidis, Alexandros ( June 2020 , Personal and Ubiquitous Computing)

   null (Ed.)

   The pervasiveness of public displays is prompting an increased need for “fresh” content to be shown, that is highly engaging and useful to passerbys. As such, live or time-sensitive content is often shown in conjunction with “traditional” static content, which creates scheduling challenges. In this work, we propose a utility-based framework that can be used to represent the usefulness of a content item over time. We develop a novel scheduling algorithm for handling live and non-live content on public displays using our utility-based framework. We experimentally evaluate our proposed algorithm against a number of alternatives under a variety of workloads; the results show that our algorithm performs well on the proposed metrics. Additional experimental evaluation shows that our utility-based framework can handle changes in priorities and deadlines of content items, without requiring any involvement by the display owner beyond the initial setup. 

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2. DeepAISE on FHIR — An Interoperable Real-Time Predictive Analytic Platform for Early Prediction of Sepsis

   Lakshman, Vidyashankar ; Amrollahi, Fatemeh ; Koppisetty, Veera Supraja ; Shashikumar, Supreeth P. ; Sharma, Ashish ; Nemati, Shamim ( October 2018 , AMIA Annual Symposium proceedings)

   Sepsis, a dysregulated immune-mediated host response to infection, is lethal, prevalent, and costly. It’s early detection has the potential to drastically reduce morbidity/mortality. We have developed a real-time cloud-based application that predicts onset-time of sepsis based on live ICU data and provides clinicians with actionable visual alerts. Clinicians and nurses can examine these alerts and initiate appropriate interventions. The prediction engine (DeepAISE) is a Deep Learning-based algorithm trained to reliably predict sepsis 4-6 hours in advance of clinical recognition. A scalable, cloud-based, system continuously streams bedside data and uses the prediction engine to generate hourly scores and displays these to clinicians. Interoperability is achieved through the use of FHIR resources and APIs. This system is monitoring ~100 patients on a daily basis at the Emory Tele-ICU center, and has been shown to reliably predict onset of sepsis with an AUC of 0.9. 

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3. Efficient Deterministic Federated Scheduling for Parallel Real-Time Tasks

   https://doi.org/10.1109/RTCSA50079.2020.9203660  

   Dinh, Son ; Gill, Christopher ; Agrawal, Kunal ( August 2020 , 2020 IEEE 26th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA))

   null (Ed.)

   Federated scheduling is a generalization of partitioned scheduling for parallel tasks on multiprocessors, and has been shown to be a competitive scheduling approach. However, federated scheduling may waste resources due to its dedicated allocation of processors to parallel tasks. In this work we introduce a novel algorithm for scheduling parallel tasks that require more than one processor to meet their deadlines (i.e., heavy tasks). The proposed algorithm computes a deterministic schedule for each heavy task based on its internal graph structure. It efficiently exploits the processors allocated to each task and thus reduces the number of processors required by the task. Experimental evaluation shows that our new federated scheduling algorithm significantly outperforms other state-of-the-art federated-based scheduling approaches, including semi-federated scheduling and reservation-based federated scheduling, that were developed to tackle resource waste in federated scheduling, and a stretching algorithm that also uses the tasks' graph structures. 

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4. Optimal Joint Offloading and Wireless Scheduling for Parallel Computing with Deadlines

   Qin, Xudong ; Xu, Weijian ; Li, Bin ( June 2019 , International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt))

   In this paper, we consider the problem of joint offloading and wireless scheduling design for parallel computing applications with hard deadlines. This is motivated by the rapid growth of compute-intensive mobile parallel computing applications (e.g., real-time video analysis, language translation) that require to be processed within a hard deadline. While there are many works on joint computing and communication algorithm design, most of them focused on the minimization of average computing time and may not be applicable for mobile applications with hard deadlines. In this work, we explicitly take hard deadlines for computing tasks into account and develop a joint offloading and scheduling algorithm based on the stochastic network optimization framework. The proposed algorithm is shown to achieve average energy consumption arbitrarily close to the optimal one. However, this algorithm involves a strong coupling between offloading and scheduling decisions, which yields significant challenges on its implementation. Towards this end, we first successfully decouple the offloading and scheduling decisions in the case with one time slot deadline by exploring the intrinsic structure of the proposed algorithm. Based on this, we further implement the proposed algorithm in the general setups. Simulations are provided to corroborate our findings. 

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5. Dead but Not Forgotten: How Extracellular DNA, Moisture, and Space Modulate the Horizontal Transfer of Extracellular Antibiotic Resistance Genes in Soil

   https://doi.org/10.1128/aem.02280-21  

   Kittredge, Heather A. ; Dougherty, Kevin M. ; Evans, Sarah E. ( April 2022 , Applied and Environmental Microbiology)

   Elkins, Christopher A. (Ed.)

   ABSTRACT Antibiotic-resistant bacteria and the spread of antibiotic resistance genes (ARGs) pose a serious risk to human and veterinary health. While many studies focus on the movement of live antibiotic-resistant bacteria to the environment, it is unclear whether extracellular ARGs (eARGs) from dead cells can transfer to live bacteria to facilitate the evolution of antibiotic resistance in nature. Here, we use eARGs from dead, antibiotic-resistant Pseudomonas stutzeri cells to track the movement of eARGs to live P. stutzeri cells via natural transformation, a mechanism of horizontal gene transfer involving the genomic integration of eARGs. In sterile, antibiotic-free agricultural soil, we manipulated the eARG concentration, soil moisture, and proximity to eARGs. We found that transformation occurred in soils inoculated with just 0.25 μg of eDNA g −1 soil, indicating that even low concentrations of soil eDNA can facilitate transformation (previous estimates suggested ∼2 to 40 μg eDNA g −1 soil). When eDNA was increased to 5 μg g −1 soil, there was a 5-fold increase in the number of antibiotic-resistant P. stutzeri cells. We found that eARGs were transformed under soil moistures typical of terrestrial systems (5 to 30% gravimetric water content) but inhibited at very high soil moistures (>30%). Overall, this work demonstrates that dead bacteria and their eARGs are an overlooked path to antibiotic resistance. More generally, the spread of eARGs in antibiotic-free soil suggests that transformation allows genetic variants to establish in the absence of antibiotic selection and that the soil environment plays a critical role in regulating transformation. IMPORTANCE Bacterial death can release eARGs into the environment. Agricultural soils can contain upwards of 10 9 ARGs g −1 soil, which may facilitate the movement of eARGs from dead to live bacteria through a mechanism of horizontal gene transfer called natural transformation. Here, we track the spread of eARGs from dead, antibiotic-resistant Pseudomonas stutzeri cells to live antibiotic-susceptible P. stutzeri cells in sterile agricultural soil. Transformation increased with the abundance of eARGs and occurred in soils ranging from 5 to 40% gravimetric soil moisture but was lowest in wet soils (>30%). Transformants appeared in soil after 24 h and persisted for up to 15 days even when eDNA concentrations were only a fraction of those found in field soils. Overall, our results show that natural transformation allows eARGs to spread and persist in antibiotic-free soils and that the biological activity of eDNA after bacterial death makes environmental eARGs a public health concern. 

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