Biomedical subjects

N Carriero

Publications and source records attributed to N Carriero.

3 recordsLinked to original sources

PubMed1997

Characteristics of smoked drug use among cocaine smokers.

The authors collected data by structured interview from a convenience sample of 228 physically healthy, largely (82%) treatment-seeking, cocaine smokers with minimal histories of other smoked (other than tobacco and marijuana) or injection drug use. The vast majority of subjects also smoked either marijuana only (17.5%), tobacco only (17%), or both (61%), with onset of such smoking almost always (97%) preceding the initiation of regular cocaine smoking. There were few significant differences in sociodemographic or cocaine use characteristics among the subgroups of subjects smoking either cocaine only or cocaine and marijuana and/or tobacco. More than one-third of marijuana smokers quit (45%) or decreased (38%) their use after starting regular cocaine smoking, whereas only 5% of tobacco smokers did so. These findings suggest that marijuana smoking is more influenced by regular cocaine smoking than is tobacco smoking.

Adult↗

PubMed1991

A parallel computing approach to genetic sequence comparison: the master-worker paradigm with interworker communication.

We have implemented a parallel version of a dynamic programming biological sequence comparison algorithm to study the potential applicability of using parallel computers for genetic sequence comparisons. Our parallel program is built using C-Linda, a machine-independent parallel programming language, and was tested on both a 10 CPU Sequent Symmetry and a 64 CPU Intel Hypercube. C-Linda implements a shared associative memory model, "tuple space," through which multiple processes can communicate and coordinate control. In our master-worker (MW) parallel implementation, a master process creates several worker processes, extracts a test sequence and multiple library sequences from a database and stores them in tuple space. Each worker reads the test sequence and then repeatedly extracts library strings from tuple space, performs pairwise sequence comparison using a local comparison algorithm to generate a similarity score, and returns the similarity scores to tuple space. The master collects the scores from tuple space and identifies the best match over all library sequences. We also implemented a method of global interworker communication to reduce the total search time by stopping those string comparisons that had no chance of improving on the current best match. Comparisons of the total run time, speedup, and efficiency were made for parallel and sequential versions of a basic MW implementation as well as versions with the global abort threshold.

Algorithms↗

PubMed1991

Parallelizing genetic linkage analysis: a case study for applying parallel computation in molecular biology.

Parallel computers offer a solution to improve the lengthy computation time of many conventional, sequential programs used in molecular biology. On a parallel computer, different pieces of the computation are performed simultaneously on different processors. LINKMAP is a sequential program widely used by scientists to perform genetic linkage analysis. We have converted LINKMAP to run on a parallel computer, using the machine-independent parallel programming language, Linda. Using the parallelization of LINKMAP as a case study, the paper outlines an approach to converting existing highly iterative programs to a parallel form. The paper describes the steps involved in converting the sequential program to a parallel program. It presents performance benchmarks comparing the sequential version of LINKMAP with the parallel version running on different parallel machines. The paper also discusses alternative approaches to the problem of "load balancing," making sure the computational load is shared as evenly as possible among the available processors.

Chromosome Mapping↗