In summary, diagnosing MDR-TB requires the use of techniques that are of limited availability and high expense for developing countries, such as culture in liquid media with DST, or line probe assays [31], [32], [33], [34], [35], [36].
However, despite the more widespread availability of diagnostic techniques that allow for earlier identification of disease (such as endoscopic ultrasound and pancreatic function testing), the treatment of CP has not changed appreciably in decades.
Most of the effort within PSS-costing literature is focused mainly on the development of techniques that address challenges regarding data availability, lifecycle representation, and uncertainty modelling.
Availability of a technique that could provide more rapid pathogen identification directly from patient samples could, therefore, represent a marked improvement in terms of enabling more rapid diagnosis and earlier initiation of appropriate antimicrobial therapy, with associated beneficial effects on outcomes, antimicrobial resistance, costs, and toxicity.
The availability of deep-sequencing techniques that produce hundreds of thousands to millions of reads of variable sizes, at only a fraction of the time and cost of Sanger sequencing, is leading to the accumulation of data in an increasing number of species.
We can define a measure-dependent notion of disorder for biased sequences only by ignoring the availability of better compression techniques that really do compress biased sequences more than unbiased ones.
The indications for adrenal biopsy have evolved with the availability of new imaging techniques that can characterize adrenal adenomas with high specificity.
Unfortunately, BPV epidemiological surveys are still limited by the availability of high-throughput diagnostic techniques that could discriminate different BPV sequences at the same time in co infected samples [ 16, 26].
The increasing availability of techniques such as ICSI means that semen containing even very low numbers of sperm should, nevertheless be cryopreserved (Lass et al, 1999).
Unsupervised learning, on the other hand, refers to the set of ML techniques that do not depend on the availability of training data; such techniques can automatically find hidden structure in unlabeled data.
