Efficacy of a monovalent human-bovine (116E) rotavirus vaccine in Indian children in the second year of life

Rotavirus gastroenteritis is one of the leading causes of diarrhea in Indian children less than 2 years of age. The 116E rotavirus strain was developed as part of the Indo-US Vaccine Action Program and has undergone efficacy trials. This paper reports the efficacy and additional safety data in children up to 2 years of age. In a double-blind placebo controlled multicenter trial, 6799 infants aged 6-7 weeks were randomized to receive three doses of an oral human-bovine natural reassortant vaccine (116E) or placebo at ages 6, 10, and 14 weeks. The primary outcome was severe (≥11 on the Vesikari scale) rotavirus gastroenteritis. Efficacy outcomes and adverse events were ascertained through active surveillance. We randomly assigned 4532 and 2267 subjects to receive vaccine and placebo, respectively, with over 96% subjects receiving all three doses of the vaccine or placebo. The per protocol analyses included 4354 subjects in the vaccine and 2187 subjects in the placebo group. The overall incidence of severe RVGE per 100 person years was 1.3 in the vaccine group and 2.9 in the placebo recipients. Vaccine efficacy against severe rotavirus gastroenteritis in children up to 2 years of age was 55.1% (95% CI 39.9 to 66.4; p<0.0001); vaccine efficacy in the second year of life of 48.9% (95% CI 17.4 to 68.4; p=0.0056) was only marginally less than in the first year of life [56.3% (95% CI 36.7 to 69.9; p<0.0001)]. The number of infants needed to be immunized to prevent one episode of severe RVGE in the first 2 years of life was 40 (95% CI 28.0 to 63.0) and for RVGE of any severity, it was 21 (95% CI 16.0 to 32.0). Serious adverse events were observed at the same rates in the two groups. None of the eight intussusception events occurred within 30 days of a vaccine dose and all were reported only after the third dose. The sustained efficacy of the 116E in the second year of life is reassuring

Fast Statistical Alignment

We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated from these models into a multiple alignment. FSA uses its explicit statistical model to produce multiple alignments which are accompanied by estimates of the alignment accuracy and uncertainty for every column and character of the alignment—previously available only with alignment programs which use computationally-expensive Markov Chain Monte Carlo approaches—yet can align thousands of long sequences. Moreover, FSA utilizes an unsupervised query-specific learning procedure for parameter estimation which leads to improved accuracy on benchmark reference alignments in comparison to existing programs. The centroid alignment approach taken by FSA, in combination with its learning procedure, drastically reduces the amount of false-positive alignment on biological data in comparison to that given by other methods. The FSA program and a companion visualization tool for exploring uncertainty in alignments can be used via a web interface at http://orangutan.math.berkeley.edu/fsa/, and the source code is available at http://fsa.sourceforge.net/

Minimizing Generalized Buchi Automata

We consider the problem of minimization of generalized Buchi automata. We show how to extend fair simulation and delayed simulation to the case where the Buchi automaton has multiple acceptance conditions. For fair simulation, we show how to efficiently compute the fair-simulation relation while maintaining the structure of the automaton. We then use the fair-simulation relation to merge states and remove transitions. Our fair-simulation algorithm works in time $O(mn^2k^2)$ where $m$ is the number of transitions, $n$ is the number of states, and $k$ is the number of acceptance sets. For delayed simulation, we extend the existing definition to the case of multiple acceptance condition. We show that our definition can indeed be used for minimization and give an algorithm that computes the delayed-simulation relation. Our delayed-simulation algorithm works in time $O(mn^2k)$. We implemented the two algorithms and report on experimental results

WISE: Automated test generation for worst-case complexity

Program analysis and automated test generation have primarily been used to find correctness bugs. We present complexity testing, a novel automated test generation technique to find performance bugs. Our complexity testing algorithm, which we call WISE (Worst-case Inputs from Symbolic Execution), operates on a program accepting inputs of arbitrary size. For each input size, WISE attempts to construct an input which exhibits the worst-case computational complexity of the program. WISE uses exhaustive test generation for small input sizes and generalizes the result of executing the program on those inputs into an “input generator.” The generator is subsequently used to efficiently generate worst-case inputs for larger input sizes. We have performed experiments to demonstrate the utility of our approach on a set of standard data structures and algorithms. Our results show that WISE can effectively generate worstcase inputs for several of these benchmarks. 1