Black soldier fly (BSF, Hermetia illucens L.) can efficiently degrade organic wastes and transform into a high fat containing insect biomass that could be used as feedstock for biodiesel production. Meanwhile, the molecular regulatory basis of fat accumulation by BSF is still unclear; it is necessary to identify vital genes and regulators that are involved in fat accumulation. This study analyzed the dynamic state of fat content and fatty-acid composition of BSF larvae in eight different stages. The late prepupa stage exhibited the highest crude fat, with lauric acid being the main component. Therefore, to provide insight into this unexplained phenomenon, the molecular regulation of rapid fat accumulation by BSF larvae was investigated. The twelve developmental stages of BSF were selected for transcriptome analysis, including the eight stages used for investigation of fat content and fatty-acid composition. By Illumina sequencing, 218,295,450,000 nt were generated. Through assembly by Trinity, 70,475 unigenes were obtained with an average length of 1064 nt and an N50 of 1749 nt. The differentially expressed unigenes were identified by DESeq, with 9159 of them being up-regulated and 10,101 of them were down-regulated. The several putative genes that are involved in the formation of pyruvate, acetyl-CoA biosynthesis, acetyl-CoA transcription, fatty-acid biosynthesis, and triacylglycerol biosynthesis were identified. The four vital metabolic genes that are associated with fat accumulation were validated by quantitative real-time PCR (qRT-PCR). The molecular mechanism of fat accumulation in BSF was clarified in this investigation through the construction of a detailed fat accumulation model from our results. The study provides an unprecedented level of insight from transcriptome sequencing to reveal the crude fat accumulation mechanism in developing BSF. The finding holds considerable promise for insectival biodiesel production, and the fat content and fatty-acid composition can be altered by genetic engineering approaches in the future for the insect production industry.

The postmortem microbiome can provide valuable information to a death investigation and to the human health of the once living. Microbiome sequencing produces, in general, large multi-dimensional datasets that can be difficult to analyze and interpret. Machine learning methods can be useful in overcoming this analytical challenge. However, different methods employ distinct strategies to handle complex datasets. It is unclear whether one method is more appropriate than others for modeling postmortem microbiomes and their ability to predict attributes of interest in death investigations, which require understanding of how the microbial communities change after death and may represent those of the once living host. Postmortem microbiomes were collected by swabbing five anatomical areas during routine death investigation, sequenced and analyzed from 188 death cases. Three machine learning methods (boosted algorithms, random forests, and neural networks) were compared with respect to their abilities to predict case attributes: postmortem interval (PMI), location of death, and manner of death. Accuracy depended on the method used, the numbers of anatomical areas analyzed, and the predicted attribute of death. All algorithms performed well but with distinct features to their performance. Xgboost often produced the most accurate predictions but may also be more prone to overfitting. Random forest was the most stable across predictions that included more anatomic areas. Analysis of postmortem microbiota from more than three anatomic areas appears to yield limited returns on accuracy, with the eyes and rectum providing the most useful information correlating with circumstances of death in most cases for this dataset.

Since traditional multiple alignment formulations are NP-hard, heuristics are commonly employed to find acceptable alignments with no guaranteed performance bound. This causes a substantial difficulty in understanding what the resulting alignment means and in assessing the quality of these alignments. We propose an alternative formulation of multiple alignment based on the idea of finding a multiple alignment of k sequences which preserves k - 1 pairwise alignments as specified by edges of a given tree. Although it is well known that such a preserving alignment always exists, it did not become a mainstream method for multiple alignment since it seems that a lot of information is lost from ignoring pairwise similarities outside the tree. In contrast, by using pairwise alignments that incorporate consistency information from other sequences, we show that it is possible to obtain very good accuracy with the preserving alignment formulation. We show that a reasonable objective function to use is to find the shortest preserving alignment, and, by a reduction to a graph-theoretic problem, that the problem of finding the shortest preserving multiple alignment can be solved in polynomial time. We demonstrate the success of this approach on three sets of benchmark multiple alignments by using consistency-based pairwise alignments from the first stage of two of the best performing progressive alignment algorithms TCoffee and ProbCons and replace the second heuristic progressive step of these algorithms by the exact preserving alignment step. We apply this strategy to TCoffee and show that our approach outperforms TCoffee on two of the three test sets. We apply the strategy to a variant of ProbCons with no iterative refinements and show that our approach achieves similar or better accuracy except on one test set. We also compare our performance to ProbCons with iterative refinements and show that our approach achieves similar or better accuracy on many subcategories even without further refinements. The most important advantage of the preserving alignment formulation is that we are certain that we can solve the problem in polynomial time without using a heuristic. A software program implementing this approach (PSAlign) is available at http://faculty.cs.tamu.edu/shsze/psalign.