Answer:
C) polysaccharide: glycosidic bond
Explanation:
Monosaccharides are the simplest carbohydrate which cannot be further hydrolysed. Examples of monosaccharides are glucose, fructose. Monosaccharides combined together to form polysaccharides. Polysaccharides are long chains of monosaccharides linked by glycosidic bonds. The glycosidic bond is a covalent bond. It is formed between an anomeric carbon of one cyclic monosaccharide with the alcoholic or OH group of a second monosaccharide. Examples of polysaccharides are starch, glycogen and cellulose.
The answer is D, all the time
Answer:
■Gene sequences would be used to make Probes for both the Southern and Northern blots.
■The probes will be used to view the presence of each gene with the use of isolated genomic DNA obtained from the isolated bacterium
■Each probes hybridized to the genome shows the pathway is isolated and point of the genes were involved in the substrate catabolism
■The carbon source in the isolate is derived from the substrate inducing the catabolic pathway as RNA determine transcripts present
■Probes hybridizing to the same sequences would be used to determine the gene activity for the pathway as seen in the southern one
■since all the genes present in the genome couldn't be identified, the northern would be important to work on
■Catabolic pathway is determined by the same genes. Hence, the need for gene/transcript probes to hybridize to the transcriptome.
Imagine you are surveying a population of a mountain range where the inhabitants live in the valleys with no inhabitants on the large mountains between. If your sample area is the valleys, and you use this to estimate the population across the entire mountain range, <u>you overestimate the actual population size</u>
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Explanation:
- An estimate that turns out to be incorrect will be an overestimate if the estimate exceeded the actual result, and an underestimate if the estimate fell short of the actual result.
- The mean of the sampling distribution of a statistic is sometimes referred to as the expected value of the statistic. Therefore the sample mean is an unbiased estimate of μ.
- Any given sample mean may underestimate or overestimate μ, but there is no systematic tendency for sample means to either under or overestimate μ.
- Bias is the tendency of a statistic to overestimate or underestimate a parameter. Bias can seep into your results for a slew of reasons including sampling or measurement errors, or unrepresentative samples
