The schizothoracine fish endemic to the Qinghai-Tibetan Plateau are comparatively well adapted to aquatic environments with low oxygen partial pressures. However, few studies have used transcriptomic profiling to investigate the adaptive responses of schizothoracine fish tissues to hypoxic stress. This study compared the transcriptomes of Gymnocypris eckloni subjected to 72 h of hypoxia (Dissolved oxygen, DO = 3.0 ± 0.1 mg/L) to those of G. eckloni under normoxia (DO = 8.4 ± 0.1 mg/L). To identify the potential genes and pathways activated in response to hypoxic stress, we collected muscle, liver, brain, heart, and blood samples from normoxic and hypoxic fish for RNA-Seq analysis. We annotated 337,481 gene fragments. Of these, 462 were differentially expressed in the hypoxic fish as compared to the normoxic fish. Under hypoxia, the transcriptomic profiles of the tissues differed, with muscle the most strongly affected by hypoxia. Our data indicated that G. eckloni underwent adaptive changes in gene expression in response to hypoxia. Several strategies used by G. eckloni to cope with hypoxia were similar to those used by other fish, including a switch from aerobic oxidation to anaerobic glycolysis and the suppression of major energy-requiring processes. However, G. eckloni used an additional distinct strategy to survive hypoxic environments: a strengthening of the antioxidant system and minimization of ischemic injury. Here, we identified several pathways and related genes involved in the hypoxic response of the schizothoracine fish. This study provides insights into the mechanisms used by schizothoracine fish to adapt to hypoxic environments.

The growth of data used by data-intensive computations, e.g. Geographical Information Systems (GIS), has far outpaced the growth of the power of a single processor. The increasing demand of data-intensive applications calls for distributed computing. In this paper, we propose a high performance workflow system MRGIS, a parallel and distributed computing platform based on MapReduce clusters, to execute GIS applications efficiently. MRGIS consists of a design interface, a task scheduler, and a runtime support system. The design interface has two options: a GUI-based workflow designer and an API-based library for programming in Python. Given a GIS workflow, the scheduler analyzes data dependencies among tasks, then dispatches them to MapReduce clusters based on the current status of the system. Our experiment demonstrates that MRGIS can significantly improve the performance of GIS workflow execution.

Abstract. The reality of multi-core hardware has made concurrent programs pervasive. Unfortunately, writing correct concurrent programs is difficult. Atomicity violation, which is caused by concurrently executing code unexpectedly violating the atomicity of a code segment, is one of the most common concurrency errors. However, atomicity violations are hard to find using traditional testing and debugging techniques. This paper presents a hybrid approach that integrates static and dynamic analyses to attack this problem. We first perform static analysis to obtain summaries of synchronizations and accesses to shared variables. The static summaries are then instantiated with runtime values during dynamic executions to speculatively approximate the behaviors of branches that are not taken. Compared to dynamic analysis, the hybrid approach is able to detect atomicity violations in unexecuted parts of the code. Compared to static analysis, the hybrid approach produces fewer false alarms. We implemented this hybrid analysis in a tool called HAVE that detects atomicity violations in multi-threaded Java programs. Experiments on several benchmarks and real-world applications demonstrate promising results.

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