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Algorithm for managing a baby with dusky soles.

S R Daga·A Gajendragadkar·R Iyer·M Swaminathan·M Doshi
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Abstract

We have been using dusky colour of the sole in newborns as proxy to cyanosis. An algorithm for management of dusky soles was developed presupposing that a baby can have dusky soles due commonly to hypothermia, hypoxaemia, or hypotension in isolation or in combination. Fifty-one consecutive babies were managed according to a predetermined algorithm based largely on clinical signs. Forty babies were covered by these three broad groups and 37 of them could be managed successfully. Of the remaining babies two were found to have congenital malformations on autopsy and one died of hyperthermia. Algorithmic approach has simplified the management with low failure rate and can be useful when hi-tech neonatal care is unaffordable.

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AlgorithmsCyanosisHumansIndiaInfant, NewbornInfant, Newborn, DiseasesProspective StudiesRisk FactorsJournal Article

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BibTeXRIS

S R Daga, A Gajendragadkar, R Iyer, M Swaminathan, M Doshi. 1995. Algorithm for managing a baby with dusky soles.. https://doi.org/10.1093/tropej%2F41.4.241

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cgDist: Nucleotide-level distance calculation from cgMLST allelic profiles.

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PubMed2026

Theseus: fast and optimal affine-gap sequence-to-graph alignment.

MOTIVATION: Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. RESULTS: This work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus processes only a subset of the dynamic programming cells, using a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: MSA and pangenome read mapping. For MSA, we compare it against SPOA, abPOA, and POASTA. Theseus is 1.6× to 17.6× faster than POASTA, and 7.3× faster, on average, than SPOA, both optimal aligners. Compared with abPOA, Theseus ensures optimality and scales to the largest problems. For pangenome read mapping, we benchmark Theseus against the alignment stage of the mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9× to 16.9× speedup on short reads. Moreover, Theseus is 1.5× to 36.3× faster than vg when aligning against synthetic cyclic graphs. AVAILABILITY AND IMPLEMENTATION: Theseus code and documentation are publicly available at https://github.com/albertjimenezbl/theseus-lib.

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