A Student\u27s Guide to giant Viruses Infecting Small Eukaryotes: From Acanthamoeba to Zooxanthellae

The discovery of infectious particles that challenge conventional thoughts concerning “what is a virus” has led to the evolution a new field of study in the past decade. Here, we review knowledge and information concerning “giant viruses”, with a focus not only on some of the best studied systems, but also provide an effort to illuminate systems yet to be better resolved. We conclude by demonstrating that there is an abundance of new host–virus systems that fall into this “giant” category, demonstrating that this field of inquiry presents great opportunities for future research

Providing Predictable Performance via a Slowdown Estimation Model

Interapplication interference at shared main memory slows down different applications differently. A few slowdown estimation models have been proposed to provide predictable performance by quantifying memory interference, but they have relatively low accuracy. Thus, we propose a more accurate slowdown estimation model called SEM at main memory. First, SEM unifies the slowdown estimation model by measuring IPC directly. Second, SEM uses the per-bank structure to monitor memory interference and improves estimation accuracy by considering write interference, row-buffer interference, and data bus interference. The evaluation results show that SEM has significantly lower slowdown estimation error (4.06%) compared to STFM (30.15%) and MISE (10.1%). </jats:p

Memory Access Scheduling Based on Dynamic Multilevel Priority in Shared DRAM Systems

Interapplication interference at shared main memory severely degrades performance and increasing DRAM frequency calls for simple memory schedulers. Previous memory schedulers employ a per-application ranking scheme for high system performance or a per-group ranking scheme for low hardware cost, but few provide a balance. We propose DMPS, a memory scheduler based on dynamic multilevel priority. First, DMPS uses “memory occupancy” to measure interference quantitatively. Second, DMPS groups applications, favors latency-sensitive groups, and dynamically prioritizes applications by employing a per-level ranking scheme. The simulation results show that DMPS has 7.2% better system performance and 22% better fairness over FRFCFS at low hardware complexity and cost.</jats:p

Influence of impeller clearance structure on volume loss of centrifugal pump

Abstract In this paper, the three-dimensional full flow field model of double volute structure centrifugal pump is established, and the ring seal, teeth seal and interlocking seal structures are set up. The K-e turbulence model of CFX software is used to simulate and analyse the fluid flow state at the gap of different mouth ring structures of centrifugal pump. The results show that the staggered ring structure can effectively reduce the leakage and improve the volumetric efficiency of centrifugal pump.</jats:p

Prediction of 2-Year Major Adverse Limb Event-Free Survival After Percutaneous Transluminal Angioplasty and Stenting for Lower Limb Atherosclerosis Obliterans: A Machine Learning-Based Study

BackgroundThe current scoring systems could not predict prognosis after endovascular therapy for peripheral artery disease. Machine learning could make predictions for future events by learning a specific pattern from existing data. This study aimed to demonstrate machine learning could make an accurate prediction for 2-year major adverse limb event-free survival (MFS) after percutaneous transluminal angioplasty (PTA) and stenting for lower limb atherosclerosis obliterans (ASO).MethodsA lower limb ASO cohort of 392 patients who received PTA and stenting was split to the training set and test set by 4:1 in chronological order. Demographic, medical, and imaging data were used to build machine learning models to predict 2-year MFS. The discrimination and calibration of artificial neural network (ANN) and random forest models were compared with the logistic regression model, using the area under the receiver operating curve (ROCAUC) with DeLong test, and the calibration curve with Hosmer–Lemeshow goodness-of-fit test, respectively.ResultsThe ANN model (ROCAUC = 0.80, 95% CI: 0.68–0.89) but not the random forest model (ROCAUC = 0.78, 95% CI: 0.66–0.87) significantly outperformed the logistic regression model (ROCAUC = 0.73, 95% CI: 0.60–0.83, P = 0.01 and P = 0.24). The ANN model the logistic regression model demonstrated good calibration performance (P = 0.73 and P = 0.28), while the random forest model showed poor calibration (P &amp;lt; 0.01). The calibration curve of the ANN model was visually the closest to the perfectly calibrated line.ConclusionMachine learning models could accurately predict 2-year MFS after PTA and stenting for lower limb ASO, in which the ANN model had better discrimination and calibration. Machine learning-derived prediction tools might be clinically useful to automatically identify candidates for PTA and stenting.</jats:sec