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There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there are few direct comparisons of relative utility. Here, we compared the performance of three DNN architectures and a classical machine learning algorithm (kernel regression) in predicting individual phenotypes from whole-brain resting-state functional connectivity (RSFC) patterns. One of the DNNs was a generic fully-connected feedforward neural network, while the other two DNNs were recently published approaches specifically designed to exploit the structure of connectome data. By using a combined sample of almost 10,000 participants from the Human Connectome Project (HCP) and UK Biobank, we showed that the three DNNs and kernel regression achieved similar performance across a wide range of behavioral and demographic measures. Furthermore, the generic feedforward neural network exhibited similar performance to the two state-of-the-art connectome-specific DNNs. When predicting fluid intelligence in the UK Biobank, performance of all algorithms dramatically improved when sample size increased from 100 to 1000 subjects. Improvement was smaller, but still significant, when sample size increased from 1000 to 5000 subjects. Importantly, kernel regression was competitive across all sample sizes. Overall, our study suggests that kernel regression is as effective as DNNs for RSFC-based behavioral prediction, while incurring significantly lower computational costs. Therefore, kernel regression might serve as a useful baseline algorithm for future studies.

Marine chitins (MC) have been utilized for the production of vast array of bioactive products, including chitooligomers, chitinase, chitosanase, antioxidants, anti-NO, and antidiabetic compounds. The aim of this study is the bioprocessing of MC into a potent anticancer compound, prodigiosin (PG), via microbial fermentation. This bioactive compound was produced by Serratia marcescens TKU011 with the highest yield of 4.62 mg/mL at the optimal conditions of liquid medium with initial pH of 5.65–6.15 containing 1% α-chitin, 0.6% casein, 0.05% K2HPO4, and 0.1% CaSO4. Fermentation was kept at 25 °C for 2 d. Notably, α-chitin was newly investigated as the major potential material for PG production via fermentation; the salt CaSO4 was also found to play the key role in the enhancement of PG yield of Serratia marcescens fermentation for the first time. PG was qualified and identified based on specific UV, MALDI-TOF MS analysis. In the biological activity tests, purified PG demonstrated potent anticancer activities against A549, Hep G2, MCF-7, and WiDr with the IC50 values of 0.06, 0.04, 0.04, and 0.2 µg/mL, respectively. Mytomycin C, a commercial anti-cancer compound was also tested for comparison purpose, showing weaker activity with the IC50 values of 0.11, 0.1, 0.14, and 0.15 µg/mL, respectively. As such, purified PG displayed higher 2.75-fold, 1.67-fold, and 3.25-fold efficacy than Mytomycin C against MCF-7, A549, and Hep G2, respectively. The results suggest that marine chitins are valuable sources for production of prodigiosin, a potential candidate for cancer drugs.

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The technology of microbial conversion provides a potential way to exploit compounds of biotechnological potential. The red pigment prodigiosin (PG) and other PG-like pigments from bacteria, majorly from Serratia marcescens, have been reported as bioactive secondary metabolites that can be used in the broad fields of agriculture, fine chemicals, and pharmacy. Increasing PG productivity by investigating the culture conditions especially the inexpensive carbon and nitrogen (C/N) sources has become an important factor for large-scale production. Investigations into the bioactivities and applications of PG and its related compounds have also been given increased attention. To save production cost, chitin and protein-containing fishery byproducts have recently been investigated as the sole C/N source for the production of PG and chitinolytic/proteolytic enzymes. This strategy provides an environmentally-friendly selection using inexpensive C/N sources to produce a high yield of PG together with chitinolytic and proteolytic enzymes by S. marcescens. The review article will provide effective references for production, bioactivity, and application of S. marcescens PG in various fields such as biocontrol agents and potential pharmaceutical drugs.