ENSEMBLE PROTEIN INFERENCE EVALUATION

The Protein inference problem is becoming an increasingly important tool that aids in the characterization of complex proteomes and analysis of complex protein samples. In bottom-up shotgun proteomics experiments the metrics for evaluation (like AUC and calibration error) are based on an often imperfect target-decoy database. These metrics make the inherent assumption that all of the proteins in the target set are present in the sample being analyzed. In general, this is not the case, they are typically a mix of present and absent proteins. To objectively evaluate inference methods, protein standard datasets are used. These datasets are special in that they have been carefully prepared to contain only the proteins specified in the target set. Though this helps, it is still unclear which metrics most adequately capture all the important aspects of a good protein inference method. In this manuscript, a novel protein standard dataset, an ensemble protein inference engine that utilizes several metrics and protein standard datasets to evaluate the performance of inference methods, and several novel protein inference methods are presented

Virtual device for driver testing

A device driver is a component of an operating system that enables the OS to recognize a certain device and communicate with it. Testing a device driver normally requires either the physical device or a software-emulated version of the device. Physical and simulated devices are intended to work correctly, e.g., they do not generally behave in an unexpected manner. This means that during testing, it is difficult or impossible to verify if the driver is able to handle unexpected behavior of a device, e.g., device behavior that is not supposed to arise in common usage but nevertheless does. Per the techniques of this disclosure, the driver-under-test is run within a virtual machine (VM). The driver communicates with a mock device controlled by programs running outside the VM. The mock device includes no actual operational logic; rather, it is scripted to simply react to driver actions, e.g., by triggering interrupts, reading/writing registers, etc. Outlier test cases of a driver, e.g., triggered by erroneous device behavior, can be tested by causing the mock device to behave in a scripted manner

Reduction of Endothelial Nitric Oxide Increases the Adhesiveness of Constitutive Endothelial Membrane ICAM-1 through Src-Mediated Phosphorylation

Nitric oxide (NO) is a known anti-adhesive molecule that prevents platelet aggregation and leukocyte adhesion to endothelial cells (ECs). The mechanism has been attributed to its role in the regulation of adhesion molecules on leukocytes and the adhesive properties of platelets. Our previous study conducted in rat venules found that reduction of EC basal NO synthesis caused EC ICAM-1-mediated firm adhesion of leukocytes within 10–30min. This quick response occurred in the absence of alterations of adhesion molecules on leukocytes and also opposes the classical pattern of ICAM-1-mediated leukocyte adhesion that requires protein synthesis and occurs hours after stimulation. The objective of this study is to investigate the underlying mechanisms of reduced basal NO-induced EC-mediated rapid leukocyte adhesion observed in intact microvessels. The relative levels of ICAM-1 at different cell regions and their activation status were determined with cellular fractionation and western blot using cultured human umbilical vein ECs. ICAM-1 adhesiveness was determined by immunoprecipitation in non-denatured proteins to assess the changes in ICAM-1 binding to its inhibitory antibody, mAb1A29, and antibody against total ICAM-1 with and without NO reduction. The adhesion strength of EC ICAM-1 was assessed by atomic force microscopy (AFM) on live cells. Results showed that reduction of EC basal NO caused by the application of caveolin-1 scaffolding domain (AP-CAV) or NOS inhibitor, L-NMMA, for 30 min significantly increased phosphorylated ICAM-1 and its binding to mAb1A29 in the absence of altered ICAM-1 expression and its distribution at subcellular regions. The Src inhibitor, PP1, inhibited NO reduction-induced increases in ICAM-1 phosphorylation and adhesive binding. AFM detected significant increases in the binding force between AP-CAV-treated ECs and mAb1A29-coated probes. These results demonstrated that reduced EC basal NO lead to a rapid increase in ICAM-1 adhesive binding via Src-mediated phosphorylation without de novo protein synthesis and translocation. This study suggests that a NO-dependent conformational change of constitutive EC membrane ICAM-1 might be the mechanism of rapid ICAM-1 dependent leukocyte adhesion observed in vivo. This new mechanistic insight provides a better understanding of EC/leukocyte interaction-mediated vascular inflammation under many disease conditions that encounter reduced basal NO in the circulation system

In-Datacenter Performance Analysis of a Tensor Processing Unit

Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, ...) that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X - 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.Comment: 17 pages, 11 figures, 8 tables. To appear at the 44th International Symposium on Computer Architecture (ISCA), Toronto, Canada, June 24-28, 201