Design and Validation of the Trailing Edge of a Variable Camber Wing Based on a Two-Dimensional Airfoil.

Variable camber wing technology stands out as the most promising morphing technology currently available in green aviation. Despite the ongoing advancements in smart materials and compliant structures, they still fall short in terms of driving force, power, and speed, rendering mechanical structures based on kinematics the preferred choice for large long-range civilian aircraft. In line with this principle, this paper introduces a linkage-based variable camber trailing edge design approach. Covering coordinated design, internal skeleton design, flexible skin design, and drive structure design, the method leverages a two-dimensional supercritical airfoil to craft a seamless, continuous two-dimensional wing full-size variable camber trailing edge structure, boasting a 2.7 m span and 4.3 m chord. Given the significant changes in aerodynamic load direction, ground tests under cruise load utilize a tracking-loading system based on tape and lever. Results indicate that the designed single-degree-of-freedom Watt I mechanism and Stephenson III drive mechanism adeptly accommodate the slender trailing edge of the supercritical airfoil. Under a maximum cruise vertical aerodynamic load of 17,072 N, the structure meets strength requirements when deflected to 5°. The research in this paper can provide some insights into the engineering design of variable camber wings.

Cancer survival prediction by learning comprehensive deep feature representation for multiple types of genetic data.

BACKGROUND: Cancer is one of the leading death causes around the world. Accurate prediction of its survival time is significant, which can help clinicians make appropriate therapeutic schemes. Cancer data can be characterized by varied molecular features, clinical behaviors and morphological appearances. However, the cancer heterogeneity problem usually makes patient samples with different risks (i.e., short and long survival time) inseparable, thereby causing unsatisfactory prediction results. Clinical studies have shown that genetic data tends to contain more molecular biomarkers associated with cancer, and hence integrating multi-type genetic data may be a feasible way to deal with cancer heterogeneity. Although multi-type gene data have been used in the existing work, how to learn more effective features for cancer survival prediction has not been well studied. RESULTS: To this end, we propose a deep learning approach to reduce the negative impact of cancer heterogeneity and improve the cancer survival prediction effect. It represents each type of genetic data as the shared and specific features, which can capture the consensus and complementary information among all types of data. We collect mRNA expression, DNA methylation and microRNA expression data for four cancers to conduct experiments. CONCLUSIONS: Experimental results demonstrate that our approach substantially outperforms established integrative methods and is effective for cancer survival prediction. AVAILABILITY AND IMPLEMENTATION: https://github.com/githyr/ComprehensiveSurvival .

Human Collective Intelligence Inspired Multi-View Representation Learning - Enabling View Communication by Simulating Human Communication Mechanism.

In real-world applications, we often encounter multi-view learning tasks where we need to learn from multiple sources of data or use multiple sources of data to make decisions. Multi-view representation learning, which can learn a unified representation from multiple data sources, is a key pre-task of multi-view learning and plays a significant role in real-world applications. Accordingly, how to improve the performance of multi-view representation learning is an important issue. In this work, inspired by human collective intelligence shown in group decision making, we introduce the concept of view communication into multi-view representation learning. Furthermore, by simulating human communication mechanism, we propose a novel multi-view representation learning approach that can fulfill multi-round view communication. Thus, each view of our approach can exploit the complementary information from other views to help with modeling its own representation, and mutual help between views is achieved. Extensive experiment results on six datasets from three significant fields indicate that our approach substantially improves the average classification accuracy by 4.536% in medicine and bioinformatics fields as well as 4.115% in machine learning field.

Joint learning sample similarity and correlation representation for cancer survival prediction.

BACKGROUND: As a highly aggressive disease, cancer has been becoming the leading death cause around the world. Accurate prediction of the survival expectancy for cancer patients is significant, which can help clinicians make appropriate therapeutic schemes. With the high-throughput sequencing technology becoming more and more cost-effective, integrating multi-type genome-wide data has been a promising method in cancer survival prediction. Based on these genomic data, some data-integration methods for cancer survival prediction have been proposed. However, existing methods fail to simultaneously utilize feature information and structure information of multi-type genome-wide data. RESULTS: We propose a Multi-type Data Joint Learning (MDJL) approach based on multi-type genome-wide data, which comprehensively exploits feature information and structure information. Specifically, MDJL exploits correlation representations between any two data types by cross-correlation calculation for learning discriminant features. Moreover, based on the learned multiple correlation representations, MDJL constructs sample similarity matrices for capturing global and local structures across different data types. With the learned discriminant representation matrix and fused similarity matrix, MDJL constructs graph convolutional network with Cox loss for survival prediction. CONCLUSIONS: Experimental results demonstrate that our approach substantially outperforms established integrative methods and is effective for cancer survival prediction.

[Enhanced ε-poly-L-lysine production through pH regulation and organic nitrogen addition in fed-batch fermentation].

During the production of ε-poly-L-lysine (ε-PL) in fed-batch fermentation, the decline of ε-PL synthesis often occurs at middle or late phase of the fermentation. To solve the problem, we adopted two strategies, namely pH shift and feeding yeast extract, to improve the productivity of ε-PL. ε-PL productivity in fermentation by pH shift and feeding yeast extract achieved 4.62 g/(L x d) and 5.16 g/(L x d), which were increased by 27.3% and 42.2% compared with the control ε-PL fed-batch fermentation, respectively. Meanwhile, ε-PL production enhanced 36.95 g/L and 41.32 g/L in 192 h with these two strategies, increased by 27.4% and 42.48% compared to the control, respectively. ε-PL production could be improved at middle or late phase of fed-batch fermentation by pH shift or feeding yeast extract.

Improvement of ε-poly-L-lysine production through seed stage development based on in situ pH monitoring.

Nissin, natamycin, and ε-poly-L-lysine (ε-PL) are three safe, microbial-produced food preservatives used today in the food industry. However, current industrial production of ε-PL is only performed in several countries. In order to realize large-scale ε-PL production by fermentation, the effects of seed stage on cell growth and ε-PL production were investigated by monitoring of pH in situ in a 5-L laboratory-scale fermenter. A significant increase in ε-PL production in fed-batch fermentation by Streptomyces sp. M-Z18 was achieved, at 48.9 g/L, through the optimization of several factors associated with seed stage, including spore pretreatment, inoculum age, and inoculum level. Compared with conventional fermentation approaches using 24-h-old shake-flask seed broth as inoculum, the maximum ε-PL concentration and productivity were enhanced by 32.3 and 36.6 %, respectively. The effect of optimized inoculum conditions on ε-PL production on a large scale was evaluated using a 50-L pilot-scale fermenter, attaining a maximum ε-PL production of 36.22 g/L in fed-batch fermentation, constituting the first report of ε-PL production at pilot scale. These results will be helpful for efficient ε-PL production by Streptomyces at pilot and plant scales.

Insights into the role of glucose and glycerol as a mixed carbon source in the improvement of ε-poly-L-lysine productivity.

Using glucose-glycerol mixed carbon source has proved to be an effective strategy for ε-poly-L-lysine (ε-PL) production with rapid cell growth and much higher ε-PL productivity. In this study, we attempt to focus on key enzymes and intracellular energy cofactors to reveal the underlying mechanisms involved in such significant improvements. The activities of key enzymes involved in the pentose phosphate pathway, TCA cycle, anaplerotic pathway and the aspartate family amino acid biosynthesis pathway as well as ε-PL synthetase showed overall enhancement with the mixed carbon source, especially in the late stages of fermentation, compared with those in either glucose or glycerol single carbon sources. Moreover, the intracellular cofactors in terms of NADH and ATP kept higher formation and consumption rates in the mixed carbon source, respectively, throughout batch fermentation. As a result, Streptomyces sp. M-Z18 could be accelerated in cell growth and precursor L-lysine biosynthesis in the mixed carbon source, thus finally shortening fermentation time and enhancing ε-PL productivity. Understanding this process will provide information for the rational regulation of the metabolism network of the quantative production of ε-PL by metabolic engineering.