Passive hemagglutination test for enteric fever

A passive hemagglutination (PHA) test for serodiagnosis of enteric fever was developed by sensitizing glutaraldehyde-preserved erythrocytes with lipopolysaccharide from Salmonella serogroups A, B, C, and D singly or simultaneously. The lipopolysaccharide-sensitized erythrocytes were tested with sera from 200 blood donors, 100 patients whose hemoculture was positive for Salmonella species, and 10 patients septicemic for other members of the family Enterobacteriaceae. The PHA test was positive in 90% of 28 acute-phase serum samples from patients with enteric fever from one hospital and in 93% of 72 acute-phase serum samples from another hospital. It was also positive in 100 and 60% of early- and late-convalescent-phase sera, respectively. The PHA test was negative in all patients septicemic for other members of the Enterobacteriaceae. Absorption of sera from patients with enteric fever with lipopolysaccharide from other members of the Enterobacteriaceae did not reduce PHA titers, indicating the specificity of the PHA test. Simultaneous sensitization with lipopolysaccharide from Salmonella serogroups A, B, C, and D was useful as a screening test in a limited trial with 28 acute-phase sera, 10 early-convalescent-phase sera, and 17 late-convalescent-phase sera. The PHA test is indeed a simple, sensitive, specific, and rapid test supplementing hemoculture in laboratory diagnosis of enteric fever.</jats:p

Managing GPU Concurrency in Heterogeneous Architectures

<p>Heterogeneous architectures consisting of general-purpose CPUs and throughput-optimized GPUs are projected to be the dominant computing platforms for many classes of applications. The design of such systems is more complex than that of homogeneous architectures because maximizing resource utilization while minimizing shared resource interference between CPU and GPU applications is difficult. We show that GPU applications tend to monopolize the shared hardware resources, such as memory and network, because of their high thread-level parallelism (TLP), and discuss the limitations of existing GPU-based concurrency management techniques when employed in heterogeneous systems. To solve this problem, we propose an integrated concurrency management strategy that modulates the TLP in GPUs to control the performance of both CPU and GPU applications. This mechanism considers both GPU core state and system-wide memory and network congestion information to dynamically decide on the level of GPU concurrency to maximize system performance. We propose and evaluate two schemes: one (CM-CPU) for boosting CPU performance in the presence of GPU interference, the other (CM-BAL) for improving both CPU and GPU performance in a balanced manner and thus overall system performance. Our evaluations show that the first scheme improves average CPU performance by 24%, while reducing average GPU performance by 11%. The second scheme provides 7% average performance improvement for both CPU and GPU applications. We also show that our solution allows the user to control performance trade-offs between CPUs and GPUs.</p