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Article ; Online: Vaccine approaches for antigen capture by liposomes

Shiqi Zhou / Yuan Luo / Jonathan Lovell

Expert Review of Vaccines, Vol 0, Iss

2023

Abstract: Introduction Liposomes have been used as carriers for vaccine adjuvants and antigens due to their inherent biocompatibility and versatility as delivery vehicles. Two vial admixture of protein antigens with liposome-formulated immunostimulatory adjuvants ... ...

Abstract	Introduction Liposomes have been used as carriers for vaccine adjuvants and antigens due to their inherent biocompatibility and versatility as delivery vehicles. Two vial admixture of protein antigens with liposome-formulated immunostimulatory adjuvants has become a broadly used clinical vaccine preparation approach. Compared to freely soluble antigens, liposome-associated forms can enhance antigen delivery to antigen-presenting cells and co-deliver antigens with adjuvants, leading to improved vaccine efficacy. Areas covered Several antigen-capture strategies for liposomal vaccines have been developed for proteins, peptides, and nucleic acids. Specific antigen delivery methodologies are discussed, including electrostatic adsorption, encapsulation inside the liposome aqueous core, and covalent and non-covalent antigen capture. Expert opinion Several commercial vaccines include active lipid components, highlighting an increasingly prominent role of liposomes and lipid nanoparticles in vaccine development. Utilizing liposomes to associate antigens offers potential advantages, including antigen and adjuvant dose-sparing, co-delivery of antigen and adjuvant to immune cells, and enhanced immunogenicity. Antigen capture by liposomes has demonstrated feasibility in clinical testing. New antigen-capture techniques have been developed and appear to be of interest for vaccine development.
Keywords	liposomes ; phospholipids ; bilayer ; antigen ; adjuvant ; qs-21 ; monophosphoryl lipid a ; lipopeptides ; Internal medicine ; RC31-1245
Subject code	610
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Taylor & Francis Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Effect of Disulfiram on the Reproductive Capacity of Female Mice

Mingming Teng / Yuan Luo / Chan Wang / Anmin Lei

International Journal of Molecular Sciences, Vol 24, Iss 2371, p

2023 Volume 2371

Abstract: In the process of assisted reproduction, the high-oxygen in vitro environment can easily cause oxidative damage to oocytes. Disulfiram (DSF) can play an anti-oxidant or pro-oxidant role in different cells, and the effect of DSF on oocytes remains unclear. ...

Abstract	In the process of assisted reproduction, the high-oxygen in vitro environment can easily cause oxidative damage to oocytes. Disulfiram (DSF) can play an anti-oxidant or pro-oxidant role in different cells, and the effect of DSF on oocytes remains unclear. Moreover, it remains unclear whether the use of DSF in the early stages of pregnancy has a negative impact on the fetus. In this study, we found that DSF increased serum FSH levels and increased the ovulation rate in mice. Moreover, DSF enhanced the antioxidant capacity of oocytes and contributed to the success rate of in vitro fertilization. Moreover, the use of DSF in early pregnancy in mice increased the uterine horn volume and the degree of vascularization, which contributed to a successful pregnancy. In addition, it was found that DSF regulated the mRNA expression of angiogenesis-related genes ( VEGF ), follicular development-related genes ( C1QTNF3 , mTOR and PI3K ), ovulation-related genes ( MAPK1 , MAPK3 and p38 MAPK ) and antioxidant-related genes ( GPX4 and CAT ). These results indicate that DSF is helpful for increasing the antioxidant capacity of oocytes and the ovulation rate. In early pregnancy in mice, DSF promotes pregnancy by increasing the degree and volume of uterine vascularization.
Keywords	disulfiram ; female mice ; reproductive capacity ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
Subject code	570
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Cascaded Convolutional Recurrent Neural Networks for EEG Emotion Recognition Based on Temporal–Frequency–Spatial Features

Yuan Luo / Changbo Wu / Caiyun Lv

Applied Sciences, Vol 13, Iss 6761, p

2023 Volume 6761

Abstract: Emotion recognition is a research area that spans multiple disciplines, including computational science, neuroscience, and cognitive psychology. The use of electroencephalogram (EEG) signals in emotion recognition is particularly promising due to their ... ...

Abstract	Emotion recognition is a research area that spans multiple disciplines, including computational science, neuroscience, and cognitive psychology. The use of electroencephalogram (EEG) signals in emotion recognition is particularly promising due to their objective and nonartefactual nature. To effectively leverage the spatial information between electrodes, the temporal correlation of EEG sequences, and the various sub-bands of information corresponding to different emotions, we construct a 4D matrix comprising temporal–frequency–spatial features as the input to our proposed hybrid model. This model incorporates a residual network based on depthwise convolution (DC) and pointwise convolution (PC), which not only extracts the spatial–frequency information in the input signal, but also reduces the training parameters. To further improve performance, we apply frequency channel attention networks (FcaNet) to distribute weights to different channel features. Finally, we use a bidirectional long short-term memory network (Bi-LSTM) to learn the temporal information in the sequence in both directions. To highlight the temporal importance of the frame window in the sample, we choose the weighted sum of the hidden layer states at all frame moments as the input to softmax. Our experimental results demonstrate that the proposed method achieves excellent recognition performance. We experimentally validated all proposed methods on the DEAP dataset, which has authoritative status in the EEG emotion recognition domain. The average accuracy achieved was 97.84% for the four binary classifications of valence, arousal, dominance, and liking and 88.46% for the four classifications of high and low valence–arousal recognition.
Keywords	emotion recognition ; electroencephalogram ; 4D features ; convolution ; attention ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Optimizing the evaluation of gene-targeted panels for tumor mutational burden estimation

Yawei Li / Yuan Luo

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 11

Abstract: Abstract Though whole exome sequencing (WES) is the gold-standard for measuring tumor mutational burden (TMB), the development of gene-targeted panels enables cost-effective TMB estimation. With the growing number of panels in clinical trials, developing ...

Abstract	Abstract Though whole exome sequencing (WES) is the gold-standard for measuring tumor mutational burden (TMB), the development of gene-targeted panels enables cost-effective TMB estimation. With the growing number of panels in clinical trials, developing a statistical method to effectively evaluate and compare the performance of different panels is necessary. The mainstream method uses R-squared value to measure the correlation between the panel-based TMB and WES-based TMB. However, the performance of a panel is usually overestimated via R-squared value based on the long-tailed TMB distribution of the dataset. Herein, we propose angular distance, a measurement used to compute the extent of the estimated bias. Our extensive in silico analysis indicates that the R-squared value reaches a plateau after the panel size reaches 0.5 Mb, which does not adequately characterize the performance of the panels. In contrast, the angular distance is still sensitive to the changes in panel sizes when the panel size reaches 6 Mb. In particular, R-squared values between the hypermutation-included dataset and the non-hypermutation dataset differ widely across many cancer types, whereas the angular distances are highly consistent. Therefore, the angular distance is more objective and logical than R-squared value for evaluating the accuracy of TMB estimation for gene-targeted panels.
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2021-10-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Unsupervised phenotyping of sepsis using nonnegative matrix factorization of temporal trends from a multivariate panel of physiological measurements

Menghan Ding / Yuan Luo

BMC Medical Informatics and Decision Making, Vol 21, Iss S5, Pp 1-

2021 Volume 15

Abstract: Abstract Background Sepsis is a highly lethal and heterogeneous disease. Utilization of an unsupervised method may identify novel clinical phenotypes that lead to targeted therapies and improved care. Methods Our objective was to derive clinically ... ...

Abstract	Abstract Background Sepsis is a highly lethal and heterogeneous disease. Utilization of an unsupervised method may identify novel clinical phenotypes that lead to targeted therapies and improved care. Methods Our objective was to derive clinically relevant sepsis phenotypes from a multivariate panel of physiological data using subgraph-augmented nonnegative matrix factorization. We utilized data from the Medical Information Mart for Intensive Care III database of patients who were admitted to the intensive care unit with sepsis. The extracted data contained patient demographics, physiological records, sequential organ failure assessment scores, and comorbidities. We applied frequent subgraph mining to extract subgraphs from physiological time series and performed nonnegative matrix factorization over the subgraphs to derive patient clusters as phenotypes. Finally, we profiled these phenotypes based on demographics, physiological patterns, disease trajectories, comorbidities and outcomes, and performed functional validation of their clinical implications. Results We analyzed a cohort of 5782 patients, derived three novel phenotypes of distinct clinical characteristics and demonstrated their prognostic implications on patient outcome. Subgroup 1 included relatively less severe/deadly patients (30-day mortality, 17%) and was the smallest-in-size group (n = 1218, 21%). It was characterized by old age (mean age, 73 years), a male majority (male-to-female ratio, 59-to-41), and complex chronic conditions. Subgroup 2 included the most severe/deadliest patients (30-day mortality, 28%) and was the second-in-size group (n = 2036, 35%). It was characterized by a male majority (male-to-female ratio, 60-to-40), severe organ dysfunction or failure compounded by a wide range of comorbidities, and uniquely high incidences of coagulopathy and liver disease. Subgroup 3 included the least severe/deadly patients (30-day mortality, 10%) and was the largest group (n = 2528, 44%). It was characterized by low age (mean age, 60 years), a ...
Keywords	Sepsis ; Phenotyping ; Physiological measurements ; Intensive care unit ; Unsupervised learning ; Clustering ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	610
Language	English
Publishing date	2021-04-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Coupling-Spirocyclization Cascade of Tryptamine-Derived Isocyanides with Iodonium Ylides and Despirocyclization Reactions.

Yuan, Luo-Rong / Ji, Shun-Jun / Xu, Xiao-Ping

Organic letters

2023 Volume 25, Issue 43, Page(s) 7858–7862

Abstract: A cobalt(II)-catalyzed coupling-cyclization cascade reaction between tryptamine-derived isocyanides and iodonium ylides is investigated, which allowed for the synthesis of different types of spiroindoline compounds by variation of substituents at the N1- ...

Abstract	A cobalt(II)-catalyzed coupling-cyclization cascade reaction between tryptamine-derived isocyanides and iodonium ylides is investigated, which allowed for the synthesis of different types of spiroindoline compounds by variation of substituents at the N1- and C2-positions in the indole skeleton. More interesting is that the spiroindoline products could undergo despirocyclization in the presence of amines, enabling efficient construction of enamine compounds.
Language	English
Publishing date	2023-10-20
Publishing country	United States
Document type	Journal Article
ISSN	1523-7052
ISSN (online)	1523-7052
DOI	10.1021/acs.orglett.3c03090
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Using Time-to-Event Model in Seed Germination Test to Evaluate Maturity during Cow Dung Composting

Yuan Luo / Xiangzhuo Meng / Yuan Liu / Kokyo Oh / Hongyan Cheng

Sustainability, Vol 15, Iss 4201, p

2023 Volume 4201

Abstract: Maturity is a matter of concern for the utilization of livestock manures after composting because of the phytotoxicity of immature compost. The seed germination test is widely used for evaluating the maturity of compost. However, the process of seed ... ...

Abstract	Maturity is a matter of concern for the utilization of livestock manures after composting because of the phytotoxicity of immature compost. The seed germination test is widely used for evaluating the maturity of compost. However, the process of seed germination was not studied by establishing a model for evaluating the maturity. Here, we established a time-to-event model for the data of germination proportion over time in a seed germination test with cow dung compost at different composting times. Results show that the profile of the seed germination proportion over time for Chinese cabbage ( Brassica rapa L.) and garden cress (Lepidium sativum L.) were both well described by the model. Seed germination was delayed in composts at the early stage of composting from parameter t 50 (half germination time) of the model. Parameter t 50 was significantly negatively related to radicle length (RL), which indicated that there is an organic relationship between seed germination (i.e., radicle emergence) and radicle elongation. In conclusion, the immature compost can hinder seed radicle elongation by delaying seed germination.
Keywords	compost maturity ; phytotoxicity ; time-to-event model ; half germination time ; radicle length ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
Subject code	580
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Key factors for species distribution modeling in benthic marine environments

Ruiju Tong / Chris Yesson / Jinsongdi Yu / Yuan Luo / Ling Zhang

Frontiers in Marine Science, Vol

2023 Volume 10

Abstract: Species distribution modeling is a widely used technique for estimating the potential habitats of target organisms based on their environmental preferences. These methods serve as valuable tools for resource managers and conservationists, and their ... ...

Abstract	Species distribution modeling is a widely used technique for estimating the potential habitats of target organisms based on their environmental preferences. These methods serve as valuable tools for resource managers and conservationists, and their utilization is increasing, particularly in marine environments where data limitations persist as a challenge. In this study, we employed the global distribution predictions of six cold-water coral species as a case study to investigate various factors influencing predictions, including modeling algorithms, background points sampling strategies and sizes, and the collinearity of environmental datasets, using both discriminative and functional performance metrics. The choice of background sampling method exhibits a stronger influence on model performance compared to the effects of modeling algorithms, background point sampling size, and the collinearity of the environmental dataset. Predictions that utilize kernel density backgrounds, maintain an equal number of presences and background points for algorithms of BRT, RF, and MARS, and employ a substantial number of background points for MAXENT, coupled with a collinearity-filtered environmental dataset in species distribution modeling, yield higher levels of discriminative and functional performance. Overall, BRT and RF outperformed MAXENT, a conclusion that is further substantiated by the analysis of smoothed residuals and the uncertainty associated with the predicted habitat suitability of Madrepora oculata. This study offers valuable insights for enhancing species distribution modeling in marine benthic environments, thereby benefiting resource management and conservation strategies for benthic species.
Keywords	species distribution modeling ; benthic marine environments ; modeling algorithms ; collinearity ; presence-only ; background points ; Science ; Q ; General. Including nature conservation ; geographical distribution ; QH1-199.5
Subject code	333
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: EEG-Based Emotion Recognition Using Convolutional Recurrent Neural Network with Multi-Head Self-Attention

Zhangfang Hu / Libujie Chen / Yuan Luo / Jingfan Zhou

Applied Sciences, Vol 12, Iss 11255, p

2022 Volume 11255

Abstract: In recent years, deep learning has been widely used in emotion recognition, but the models and algorithms in practical applications still have much room for improvement. With the development of graph convolutional neural networks, new ideas for emotional ...

Abstract	In recent years, deep learning has been widely used in emotion recognition, but the models and algorithms in practical applications still have much room for improvement. With the development of graph convolutional neural networks, new ideas for emotional recognition based on EEG have arisen. In this paper, we propose a novel deep learning model-based emotion recognition method. First, the EEG signal is spatially filtered by using the common spatial pattern (CSP), and the filtered signal is converted into a time–frequency map by continuous wavelet transform (CWT). This is used as the input data of the network; then the feature extraction and classification are performed by the deep learning model. We called this model CNN-BiLSTM-MHSA, which consists of a convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM), and multi-head self-attention (MHSA). This network is capable of learning the time series and spatial information of EEG emotion signals in depth, smoothing EEG signals and extracting deep features with CNN, learning emotion information of future and past time series with BiLSTM, and improving recognition accuracy with MHSA by reassigning weights to emotion features. Finally, we conducted experiments on the DEAP dataset for sentiment classification, and the experimental results showed that the method has better results than the existing classification. The accuracy of high and low valence, arousal, dominance, and liking state recognition is 98.10%, and the accuracy of four classifications of high and low valence-arousal recognition is 89.33%.
Keywords	EEG ; emotion recognition ; CNN ; BiLSTM ; multi-head self-attention ; time–frequency map ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2022-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Statistical and machine learning methods for spatially resolved transcriptomics data analysis

Zexian Zeng / Yawei Li / Yiming Li / Yuan Luo

Genome Biology, Vol 23, Iss 1, Pp 1-

2022 Volume 23

Abstract: Abstract The recent advancement in spatial transcriptomics technology has enabled multiplexed profiling of cellular transcriptomes and spatial locations. As the capacity and efficiency of the experimental technologies continue to improve, there is an ... ...

Abstract	Abstract The recent advancement in spatial transcriptomics technology has enabled multiplexed profiling of cellular transcriptomes and spatial locations. As the capacity and efficiency of the experimental technologies continue to improve, there is an emerging need for the development of analytical approaches. Furthermore, with the continuous evolution of sequencing protocols, the underlying assumptions of current analytical methods need to be re-evaluated and adjusted to harness the increasing data complexity. To motivate and aid future model development, we herein review the recent development of statistical and machine learning methods in spatial transcriptomics, summarize useful resources, and highlight the challenges and opportunities ahead.
Keywords	Biology (General) ; QH301-705.5 ; Genetics ; QH426-470
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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