Restriction Enzyme Analysis of Ribosomal DNA Shows that Candida inconspicua Clinical Isolates Can Be Misidentified as Candida norvegensis with Traditional Diagnostic Procedures

We identified 29 yeast isolates from 22 patients using the API ID32C panel. Twenty-eight of these isolates were Candida norvegensis and one was C. inconspicua. Although C. norvegensis is considered a pseudohypha-producing species, only one isolate produced pseudohyphae. Restriction enzyme analysis of PCR-amplified ribosomal DNA with four different enzymes proved that all isolates were C. inconspicua

Efficient parallelized network coding for P2P file sharing applications

Abstract. In this paper, we investigate parallel implementation techniques for network coding to enhance the performance of Peer-to-Peer (P2P) file sharing applications. It is known that network coding mitigates peer/piece selection problems in P2P file sharing systems; however, due to the decoding complexity of network coding, there have been concerns about adoption of network coding in P2P file sharing systems and to improve the decoding speed the exploitation of parallelism has been proposed previously. In this paper, we argue that naive parallelization strategies of network coding may result in unbalanced workload distribution and thus limiting performance improvements. We further argue that higher performance enhancement can be achieved through load balancing in parallelized network coding and propose new parallelization techniques for network coding. Our experiments show that, on a quad-core processor system, proposed algorithms exhibit up to 30 % of speed-up compared to an existing approach using 1 Mbytes data with 2048×2048 coefficient matrix size

Discovery of Lung Cancer Biomarkers by Profiling the Plasma Proteome with Monoclonal Antibody Libraries*

A challenge in the treatment of lung cancer is the lack of early diagnostics. Here, we describe the application of monoclonal antibody proteomics for discovery of a panel of biomarkers for early detection (stage I) of non-small cell lung cancer (NSCLC). We produced large monoclonal antibody libraries directed against the natural form of protein antigens present in the plasma of NSCLC patients. Plasma biomarkers associated with the presence of lung cancer were detected via high throughput ELISA. Differential profiling of plasma proteomes of four clinical cohorts, totaling 301 patients with lung cancer and 235 healthy controls, identified 13 lung cancer-associated (p < 0.05) monoclonal antibodies. The monoclonal antibodies recognize five different cognate proteins identified using immunoprecipitation followed by mass spectrometry. Four of the five antigens were present in non-small cell lung cancer cells in situ. The approach is capable of generating independent antibodies against different epitopes of the same proteins, allowing fast translation to multiplexed sandwich assays. Based on these results, we have verified in two independent clinical collections a panel of five biomarkers for classifying patient disease status with a diagnostics performance of 77% sensitivity and 87% specificity. Combining CYFRA, an established cancer marker, with the panel resulted in a performance of 83% sensitivity at 95% specificity for stage I NSCLC