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Abstract The current study evaluated the potential enhancement of lauric acid (LA) in black soldier fly, Hermetia illucens, (L.) (Diptera: Stratiomyidae) larvae (BSFL), a source of this short-chain fatty acid which has antimicrobial and immunostimulatory properties. Replicate groups of BSFL were reared on either the coconut or Gainesville diet for 7 days. After the rearing period, BSFL were harvested, purged, dried, and subjected to proximate, fatty acid and amino acid compositions, and pepsin digestibility analyses. Results demonstrate changes in proximate composition. BSFL reared on the coconut had significantly (P = 0.002) higher lipid content (47.3% vs. 25.2%) on a dry-matter basis. The LA concentration in BSFL produced on the coconut was 31% greater than those reared on Gainesville, resulting in almost 150% more LA. Furthermore, BSFL-fed coconut had reduced crude protein (29.7% of dry weight) and ash (3.7% of dry weight) relative to those fed Gainesville (43.4% and 7.5% for crude protein and ash, respectively) but higher pepsin digestibility (91.0% vs. 87.0%). The relative amounts of various amino acids in the 2 BSFL meals did not differ extensively, with statistically lower concentrations of only phenylalanine and tryptophan and higher concentrations of alanine, arginine, isoleucine, leucine, and serine in BSFL reared on coconut. Results demonstrate that the nutritional composition of BSFL can be manipulated, and an enhancement of LA concentrations of 150% was achieved with coconut, which has value for BSFL as a feed for various livestock, including aquaculture. Lower protein content is a tradeoff in terms of BSFL value as a feed additive. 

RNA expression profiles contain information about the state of a cell and specific gene expression changes are often associated with disease. Classification of blood or similar samples based on RNA expression can thus be a powerful method for disease diagnosis. However, basing diagnostic decisions on RNA expression remains impractical for most clinical applications because it requires costly and slow gene expression profiling based on microarrays or next generation sequencing followed by often complex in silico analysis. DNA-based molecular classifiers that perform a computation over RNA inputs and summarize a diagnostic result in situ have been developed to address this issue, but lack the sensitivity required for use with actual biological samples. To address this limitation, we here propose a DNA-based classification system that takes advantage of PCR-based amplification for increased sensitivity. In our initial scheme, the importance of a transcript for a diagnostic decision is proportional to the number of molecular probes bound to that transcript. Although probe concentration is similar to that of the RNA input, subsequent amplification of the probes with PCR can dramatically increase the sensitivity of the assay. However, even slight biases in PCR efficiency can distort weight information encoded by the original probe set. To address this concern, we developed and mathematically analyzed multiple strategies for mitigating the bias associated with PCR-based amplification. We evaluate these amplified molecular classification strategies through simulation using two distinct gene expression data sets and associated disease categories as inputs. Through this analysis, we arrive at a novel molecular classifier framework that naturally accommodates PCR bias and also uses a smaller number of molecular probes than required in the initial, naive implementation.