Exploring Multiple-Objective Optimization for Efficient and Effective Test Paper Design with Dynamic Programming Guided Genetic Algorithm.

Automatic test paper design is critical in education to reduce workloads for educators and facilitate an efficient teaching process. However, current designs fail to satisfy the realistic teaching requirements of educators, including the consideration of both test quality and efficiency. This is the main reason why teachers still manually construct tests in most teaching environments. In this paper, the quality of tests is quantitatively defined while considering multiple objectives, including a flexible coverage of knowledge points, cognitive levels, and question difficulty. Then, a model based on the technique of linear programming is delicately designed to explore the optimal results for this newly defined problem. However, this technique is not efficient enough, which cannot obtain results in polynomial time. With the consideration of both test quality and generation efficiency, this paper proposes a genetic algorithm (GA) based method, named dynamic programming guided genetic algorithm with adaptive selection (DPGA-AS). In this method, a dynamic programming method is proposed in the population initialization part to improve the efficiency of the genetic algorithm. An adaptive selection method for the GA is designed to avoid prematurely falling into the local optimal for better test quality. The question bank used in our experiments is assembled based on college-level calculus questions from well-known textbooks. The experimental results show that the proposed techniques can construct test papers with both high effectiveness and efficiency. The computation time of the test assembly problem is reduced from 3 hours to 2 seconds for a 5000-size question bank as compared to a linear programming model with similar test quality. The test quality of the proposed method is better than the other baselines.

D- and Unnatural Amino Acid Substituted Antimicrobial Peptides With Improved Proteolytic Resistance and Their Proteolytic Degradation Characteristics.

The transition of antimicrobial peptides (AMPs) from the laboratory to market has been severely hindered by their instability toward proteases in biological systems. In the present study, we synthesized derivatives of the cationic AMP Pep05 (KRLFKKLLKYLRKF) by substituting L-amino acid residues with D- and unnatural amino acids, such as D-lysine, D-arginine, L-2,4-diaminobutanoic acid (Dab), L-2,3-diaminopropionic acid (Dap), L-homoarginine, 4-aminobutanoic acid (Aib), and L-thienylalanine, and evaluated their antimicrobial activities, toxicities, and stabilities toward trypsin, plasma proteases, and secreted bacterial proteases. In addition to measuring changes in the concentration of the intact peptides, LC-MS was used to identify the degradation products of the modified AMPs in the presence of trypsin and plasma proteases to determine degradation pathways and examine whether the amino acid substitutions afforded improved proteolytic resistance. The results revealed that both D- and unnatural amino acids enhanced the stabilities of the peptides toward proteases. The derivative DP06, in which all of the L-lysine and L-arginine residues were replaced by D-amino acids, displayed remarkable stability and mild toxicity in vitro but only slight activity and severe toxicity in vivo, indicating a significant difference between the in vivo and in vitro results. Unexpectedly, we found that the incorporation of a single Aib residue at the N-terminus of compound UP09 afforded remarkably enhanced plasma stability and improved activity in vivo. Hence, this derivative may represent a candidate AMP for further optimization, providing a new strategy for the design of novel AMPs with improved bioavailability.

Recognition of Invasive Prostate Cancer Using a GHRL Polypeptide Probe Targeting GHSR in a Mouse Model In Vivo.

BACKGROUND: Ghrelin (GHRL) is a polypeptide that can specifically bind to the growth hormone secretagogue receptor (GHSR). The expression of GHSR is significantly different in normal and prostate cancer (PC) tissues in humans. It is important to find an effective diagnostic method for the diagnosis and prognosis of invasive PC/neuroendocrine prostate cancer (NEPC). METHODS: GHRL and GHSR mRNA levels were determined by a quantitative real-time polymerase chain reaction in PC tissues. The expression of GHRL and GHSR proteins was assessed by Western blot assay and immunohistochemistry. A GHRL polypeptide probe was synthesized by standard solid-phase polypeptide synthesis, and labeled with Alexa Fluor 660. Confocal microscopy was used to capture fluorescence images. Living imaging analysis showed tumor areas of different invasiveness in mice models. RESULTS: The levels of GHRL and GHSR copy number amplification and mRNA expression were increased in invasive PC/NEPC, and the protein expression levels of GHRL and GHSR were similarly increased in NEPC. The GHRL polypeptide probe could effectively bind to GHSR. In PC3 cells, it was found that the GHRL probe specifically binds to GHSR on the cell membrane and accumulates in the cells through internalization after binding. Live imaging in mice models showed that there were different signal intensities in tumor areas with different invasiveness. CONCLUSION: GHSR and GHRL might be used in molecular imaging diagnosis for invasive PC/NEPC in the future.