BackgroundAdenoviruses force quiescent cells to re-enter the cell cycle to replicate their DNA, and for the most part, this is accomplished after they express the E1A protein immediately after infection. In this context, E1A is believed to inactivate cellular proteins (e.g., p130) that are known to be involved in the silencing of E2F-dependent genes that are required for cell cycle entry. However, the potential perturbation of these types of genes by E1A relative to their functions in regulatory networks and canonical pathways remains poorly understood.FindingsWe have used DNA microarrays analyzed with Bayesian ANOVA for microarray (BAM) to assess changes in gene expression after E1A alone was introduced into quiescent cells from a regulated promoter. Approximately 2,401 genes were significantly modulated by E1A, and of these, 385 and 1033 met the criteria for generating networks and functional and canonical pathway analysis respectively, as determined by using Ingenuity Pathway Analysis software. After focusing on the highest-ranking cellular processes and regulatory networks that were responsive to E1A in quiescent cells, we observed that many of the up-regulated genes were associated with DNA replication, the cell cycle and cellular compromise. We also identified a cadre of up regulated genes with no previous connection to E1A; including genes that encode components of global DNA repair systems and DNA damage checkpoints. Among the down-regulated genes, we found that many were involved in cell signalling, cell movement, and cellular proliferation. Remarkably, a subset of these was also associated with p53-independent apoptosis, and the putative suppression of this pathway may be necessary in the viral life cycle until sufficient progeny have been produced.ConclusionsThese studies have identified for the first time a large number of genes that are relevant to E1A's activities in promoting quiescent cells to re-enter the cell cycle in order to create an optimum environment for adenoviral replication.
Machine Learning graphs (or models) can be challenging or impossible to train when either devices have limited memory, or the models are large. Splitting the model graph across multiple devices, today, largely relies on learning-based approaches to generate this placement. While it results in models that train fast on data (i.e., with low step times), learning-based model-parallelism is time-consuming, taking many hours or days to create a placement plan of operators on devices. We present the Baechi system, where we adopt an algorithmic approach to the placement problem for running machine learning training graphs on a small cluster of memory-constrained devices. We implemented Baechi so that it works modularly with TensorFlow. Our experimental results using GPUs show that Baechi generates placement plans in time 654×-206K × faster than today's learning-based approaches, and the placed model's step time is only up to 6.2% higher than expert-based placements. CCS CONCEPTS • Computer systems organization → Distributed architectures.
A recent breakthrough of Ashlagi, Kanoria, and Leshno [AKL17] found that imbalance in the number of agents on either side of a random matching market has a profound effect on the expected characteristics of the market. Specifically, across all stable matchings, the "long side" (i.e. the side with a greater number of agents) receives significantly worse matches in expectation than the short side. If matchings are found via the classic one-side proposing deferred acceptance algorithm, this indicates that the difference between the proposing and the receiving side is essentially unimportant compared to the difference between the long and the short side.We provide new intuition and a new proof for preliminary results in the direction of [AKL17], namely calculating the expected rank that an agent on the long side has for their optimal stable match.
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