Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals.

We propose a framework for modeling sequence motifs based on the maximum entropy principle (MEP). We recommend approximating short sequence motif distributions with the maximum entropy distribution (MED) consistent with low-order marginal constraints estimated from available data, which may include dependencies between nonadjacent as well as adjacent positions. Many maximum entropy models (MEMs) are specified by simply changing the set of constraints. Such models can be utilized to discriminate between signals and decoys. Classification performance using different MEMs gives insight into the relative importance of dependencies between different positions. We apply our framework to large datasets of RNA splicing signals. Our best models out-perform previous probabilistic models in the discrimination of human 5' (donor) and 3' (acceptor) splice sites from decoys. Finally, we discuss mechanistically motivated ways of comparing models.     

Emergency Resection in Treatment of Diverticular Disease of Colon Complicated by Peritonitis

Of a consecutive series of 25 patients with peritonitis secondary to colonic diverticular disease all, except one with faecal peritonitis, underwent some form of emergency resection.All the three patients with faecal peritonitis died, but the 22 with purulent peritonitis survived. The average duration of the emergency admission of the 22 survivors was 25.4 days, and in nine (41%) of them intestinal continuity had been restored by the end of that admission.Thus some form of emergency resection is the operation of choice in patients with spreading peritonitis due to diverticular disease of the sigmoid colon.     

Genome Sequence of Multidrug-Resistant Escherichia coli EC302/04, Isolated from a Human Tracheal Aspirate

Escherichia coli is an important etiologic agent of lower respiratory tract infections (LRTI). Multidrug-resistant E. coli EC302/04 was isolated from a tracheal aspirate, and its genome sequence is expected to provide insights into antimicrobial resistance as well as adaptive and virulence mechanisms of E. coli involved in LRTI.