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Article ; Online: Differential responses of rumen and fecal fermentation and microbiota of Liaoning cashmere goats after 2-hydroxy-4-(methylthio) butanoic acid isopropyl ester supplementation.

Zhong, Zhiqiang / Sun, Peiyuan / Zhang, Yuning / Li, Lingyun / Han, Di / Pan, Xiaoguang / Zhang, Ruiyang

2024 Volume 14, Issue 1, Page(s) 8505

Abstract: The 2-hydroxy-4-(methylthio) butanoic acid isopropyl ester (HMBi), a rumen protective methionine, has been extensively studied in dairy cows and beef cattle and has been shown to regulate gastrointestinal microbiota and improve production performance. ... ...

Abstract	The 2-hydroxy-4-(methylthio) butanoic acid isopropyl ester (HMBi), a rumen protective methionine, has been extensively studied in dairy cows and beef cattle and has been shown to regulate gastrointestinal microbiota and improve production performance. However, knowledge of the application of HMBi on cashmere goats and the simultaneous study of rumen and hindgut microbiota is still limited. In this study, HMBi supplementation increased the concentration of total serum protein, the production of microbial protein in the rumen and feces, as well as butyrate production in the feces. The results of PCoA and PERMANOVA showed no significant difference between the rumen microbiota, but there was a dramatic difference between the fecal microbiota of the two groups of Cashmere goats after the HMBi supplementation. Specifically, in the rumen, HMBi significantly increased the relative abundance of some fiber-degrading bacteria (such as Fibrobacter) compared with the CON group. In the feces, as well as a similar effect as in the rumen (increasing the relative abundance of some fiber-degrading bacteria, such as Lachnospiraceae FCS020 group and ASV32), HMBi diets also increased the proliferation of butyrate-producing bacteria (including Oscillospiraceae UCG-005 and Christensenellaceae R-7 group). Overall, these results demonstrated that HMBi could regulate the rumen and fecal microbial composition of Liaoning cashmere goats and benefit the host.
MeSH term(s)	Animals ; Cattle ; Female ; Butyric Acid/pharmacology ; Butyric Acid/metabolism ; Esters/metabolism ; Rumen/microbiology ; Fermentation ; Goats ; Diet/veterinary ; Feces ; Bacteria/metabolism ; Dietary Supplements ; Microbiota ; Animal Feed/analysis ; Lactation/physiology
Chemical Substances	Butyric Acid (107-92-6) ; Esters
Language	English
Publishing date	2024-04-12
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-58581-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Exploring Spillover Effects for COVID-19 Cascade Prediction.

Chen, Ninghan / Chen, Xihui / Zhong, Zhiqiang / Pang, Jun

Entropy (Basel, Switzerland)

2022 Volume 24, Issue 2

Abstract: An information outbreak occurs on social media along with the COVID-19 pandemic and leads to ... ...

Abstract	An information outbreak occurs on social media along with the COVID-19 pandemic and leads to an
Language	English
Publishing date	2022-01-31
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e24020222
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Book ; Online: Knowledge-augmented Graph Machine Learning for Drug Discovery

Zhong, Zhiqiang / Barkova, Anastasia / Mottin, Davide

A Survey from Precision to Interpretability

2023

Abstract: The integration of Artificial Intelligence (AI) into the field of drug discovery has been a growing area of interdisciplinary scientific research. However, conventional AI models are heavily limited in handling complex biomedical structures (such as 2D ... ...

Abstract	The integration of Artificial Intelligence (AI) into the field of drug discovery has been a growing area of interdisciplinary scientific research. However, conventional AI models are heavily limited in handling complex biomedical structures (such as 2D or 3D protein and molecule structures) and providing interpretations for outputs, which hinders their practical application. As of late, Graph Machine Learning (GML) has gained considerable attention for its exceptional ability to model graph-structured biomedical data and investigate their properties and functional relationships. Despite extensive efforts, GML methods still suffer from several deficiencies, such as the limited ability to handle supervision sparsity and provide interpretability in learning and inference processes, and their ineffectiveness in utilising relevant domain knowledge. In response, recent studies have proposed integrating external biomedical knowledge into the GML pipeline to realise more precise and interpretable drug discovery with limited training instances. However, a systematic definition for this burgeoning research direction is yet to be established. This survey presents a comprehensive overview of long-standing drug discovery principles, provides the foundational concepts and cutting-edge techniques for graph-structured data and knowledge databases, and formally summarises Knowledge-augmented Graph Machine Learning (KaGML) for drug discovery. we propose a thorough review of related KaGML works, collected following a carefully designed search methodology, and organise them into four categories following a novel-defined taxonomy. To facilitate research in this promptly emerging field, we also share collected practical resources that are valuable for intelligent drug discovery and provide an in-depth discussion of the potential avenues for future advancements.
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2023-02-16
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Unsupervised Network Embedding Beyond Homophily

Zhong, Zhiqiang / Gonzalez, Guadalupe / Grattarola, Daniele / Pang, Jun

2022

Abstract: Network embedding (NE) approaches have emerged as a predominant technique to represent complex networks and have benefited numerous tasks. However, most NE approaches rely on a homophily assumption to learn embeddings with the guidance of supervisory ... ...

Abstract	Network embedding (NE) approaches have emerged as a predominant technique to represent complex networks and have benefited numerous tasks. However, most NE approaches rely on a homophily assumption to learn embeddings with the guidance of supervisory signals, leaving the unsupervised heterophilous scenario relatively unexplored. This problem becomes especially relevant in fields where a scarcity of labels exists. Here, we formulate the unsupervised NE task as an r-ego network discrimination problem and develop the SELENE framework for learning on networks with homophily and heterophily. Specifically, we design a dual-channel feature embedding pipeline to discriminate r-ego networks using node attributes and structural information separately. We employ heterophily adapted self-supervised learning objective functions to optimise the framework to learn intrinsic node embeddings. We show that SELENE's components improve the quality of node embeddings, facilitating the discrimination of connected heterophilous nodes. Comprehensive empirical evaluations on both synthetic and real-world datasets with varying homophily ratios validate the effectiveness of SELENE in homophilous and heterophilous settings showing an up to 12.52% clustering accuracy gain. Comment: Accepted to Transactions on Machine Learning Research
Keywords	Computer Science - Social and Information Networks ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2022-03-21
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation

Zhong, Zhiqiang / Ivanov, Sergey / Pang, Jun

2022

Abstract: Graph Neural Networks (GNNs) have been predominant for graph learning tasks; however, recent studies showed that a well-known graph algorithm, Label Propagation (LP), combined with a shallow neural network can achieve comparable performance to GNNs in ... ...

Abstract	Graph Neural Networks (GNNs) have been predominant for graph learning tasks; however, recent studies showed that a well-known graph algorithm, Label Propagation (LP), combined with a shallow neural network can achieve comparable performance to GNNs in semi-supervised node classification on graphs with high homophily. In this paper, we show that this approach falls short on graphs with low homophily, where nodes often connect to the nodes of the opposite classes. To overcome this, we carefully design a combination of a base predictor with LP algorithm that enjoys a closed-form solution as well as convergence guarantees. Our algorithm first learns the class compatibility matrix and then aggregates label predictions using LP algorithm weighted by class compatibilities. On a wide variety of benchmarks, we show that our approach achieves the leading performance on graphs with various levels of homophily. Meanwhile, it has orders of magnitude fewer parameters and requires less execution time. Empirical evaluations demonstrate that simple adaptations of LP can be competitive in semi-supervised node classification in both homophily and heterophily regimes.
Keywords	Computer Science - Machine Learning ; Computer Science - Social and Information Networks
Subject code	006
Publishing date	2022-05-19
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: "Double vaccinated, 5G boosted!"

Chen, Ninghan / Chen, Xihui / Zhong, Zhiqiang / Pang, Jun

Learning Attitudes towards COVID-19 Vaccination from Social Media

2022

Abstract: To address the vaccine hesitancy which impairs the efforts of the COVID-19 vaccination campaign, it is imperative to understand public vaccination attitudes and timely grasp their changes. In spite of reliability and trustworthiness, conventional ... ...

Abstract	To address the vaccine hesitancy which impairs the efforts of the COVID-19 vaccination campaign, it is imperative to understand public vaccination attitudes and timely grasp their changes. In spite of reliability and trustworthiness, conventional attitude collection based on surveys is time-consuming and expensive, and cannot follow the fast evolution of vaccination attitudes. We leverage the textual posts on social media to extract and track users' vaccination stances in near real time by proposing a deep learning framework. To address the impact of linguistic features such as sarcasm and irony commonly used in vaccine-related discourses, we integrate into the framework the recent posts of a user's social network neighbours to help detect the user's genuine attitude. Based on our annotated dataset from Twitter, the models instantiated from our framework can increase the performance of attitude extraction by up to 23% compared to state-of-the-art text-only models. Using this framework, we successfully validate the feasibility of using social media to track the evolution of vaccination attitudes in real life. We further show one practical use of our framework by validating the possibility to forecast a user's vaccine hesitancy changes with information perceived from social media.
Keywords	Computer Science - Social and Information Networks ; Computer Science - Computers and Society ; Computer Science - Machine Learning
Subject code	006
Publishing date	2022-06-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Differential Responses of Digesta- and Mucosa-Associated Jejunal Microbiota of Hu Sheep to Pelleted and Non-Pelleted High-Grain Diets

Zhong, Zhiqiang / Zhang, Yuning / Li, Xiaotong / Li, Lingyun / Zhang, Ruiyang / Zhang, Shuyi

Animals. 2022 June 30, v. 12, no. 13

2022

Abstract: In the present study, we utilized 16S rRNA sequencing to uncover the impacts of non-pelleted (HG) or high-grain pelleted (HP) diets on the microbial structure and potential functions of digesta- and mucosa-associated microbiota in the jejunum of Hu sheep. ...

Abstract	In the present study, we utilized 16S rRNA sequencing to uncover the impacts of non-pelleted (HG) or high-grain pelleted (HP) diets on the microbial structure and potential functions of digesta- and mucosa-associated microbiota in the jejunum of Hu sheep. Here, we randomly assigned 15 healthy male Hu sheep into three groups and fed the control diets (CON), HG, and HP diets, respectively. The experiment period was 60 days. The HP diets had the same nutritional ingredients as the HG diets but in pelleted form. At the finish of the experiment, the jejunal digesta and mucosa were gathered for microbial sequencing. The results of PCoA and PERMANOVA showed that different dietary treatments had significant impact (p < 0.05) on digesta- and mucosa-associated microbiota in the jejunum of Hu sheep. For specific differences, HG diets significantly increased (p < 0.05) the abundance of some acid-producing bacteria in both jejunal digesta (Bifidobacterium, OTU151, and OTU16) and mucosa (Rikenellaceae RC9 gut group, and Bifidobacterium) of Hu sheep compared with the CON diets. Besides the similar effects of the HG diets (increased the acid-producing bacteria such as Olsenella, Pseudoramibacter, and Shuttleworthia), our results also showed that the HP diets significantly decreased (p < 0.05) the abundance of some pro-inflammatory bacteria in the jejunal digesta (Mogibacterium, and Marvinbryantia) and mucosa (Chitinophaga, and Candidatus Saccharimonas) of Hu sheep compared with the HG diets. Collectively, these findings contributed to enriching the knowledge about the effects of HG diets on the structure and function of intestinal microbiota in ruminants.
Keywords	Bifidobacterium ; Chitinophaga ; Mogibacterium ; Olsenella ; Pseudoramibacter ; digesta ; intestinal microorganisms ; jejunum ; males ; mucosa ; sheep
Language	English
Dates of publication	2022-0630
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2606558-7
ISSN	2076-2615
ISSN	2076-2615
DOI	10.3390/ani12131695
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Pseudo-Chédiak-Higashi inclusions in relapsed acute lymphoblastic leukaemia.

Zhong, Zhi-Qiang / Zhuang, Hai-Feng / Wu, Shu-Ling / Zhang, Hang

British journal of haematology

2021 Volume 195, Issue 3, Page(s) 300

MeSH term(s)	Allografts ; Azure Stains ; Biomarkers, Tumor ; Bone Marrow/pathology ; Child ; Hematopoietic Stem Cell Transplantation ; Humans ; Inclusion Bodies/ultrastructure ; Male ; Neoplastic Cells, Circulating ; Neoplastic Stem Cells/ultrastructure ; Precursor B-Cell Lymphoblastic Leukemia-Lymphoma/blood ; Precursor B-Cell Lymphoblastic Leukemia-Lymphoma/pathology ; Precursor B-Cell Lymphoblastic Leukemia-Lymphoma/therapy ; Recurrence ; Staining and Labeling
Chemical Substances	Azure Stains ; Biomarkers, Tumor
Language	English
Publishing date	2021-06-24
Publishing country	England
Document type	Case Reports ; Journal Article
ZDB-ID	80077-6
ISSN	1365-2141 ; 0007-1048
ISSN (online)	1365-2141
ISSN	0007-1048
DOI	10.1111/bjh.17635
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Article: Differential Responses of Digesta- and Mucosa-Associated Jejunal Microbiota of Hu Sheep to Pelleted and Non-Pelleted High-Grain Diets.

Zhong, Zhiqiang / Zhang, Yuning / Li, Xiaotong / Li, Lingyun / Zhang, Ruiyang / Zhang, Shuyi

Animals : an open access journal from MDPI

2022 Volume 12, Issue 13

Abstract	In the present study, we utilized 16S rRNA sequencing to uncover the impacts of non-pelleted (HG) or high-grain pelleted (HP) diets on the microbial structure and potential functions of digesta- and mucosa-associated microbiota in the jejunum of Hu sheep. Here, we randomly assigned 15 healthy male Hu sheep into three groups and fed the control diets (CON), HG, and HP diets, respectively. The experiment period was 60 days. The HP diets had the same nutritional ingredients as the HG diets but in pelleted form. At the finish of the experiment, the jejunal digesta and mucosa were gathered for microbial sequencing. The results of PCoA and PERMANOVA showed that different dietary treatments had significant impact (p < 0.05) on digesta- and mucosa-associated microbiota in the jejunum of Hu sheep. For specific differences, HG diets significantly increased (p < 0.05) the abundance of some acid-producing bacteria in both jejunal digesta (Bifidobacterium, OTU151, and OTU16) and mucosa (Rikenellaceae RC9 gut group, and Bifidobacterium) of Hu sheep compared with the CON diets. Besides the similar effects of the HG diets (increased the acid-producing bacteria such as Olsenella, Pseudoramibacter, and Shuttleworthia), our results also showed that the HP diets significantly decreased (p < 0.05) the abundance of some pro-inflammatory bacteria in the jejunal digesta (Mogibacterium, and Marvinbryantia) and mucosa (Chitinophaga, and Candidatus Saccharimonas) of Hu sheep compared with the HG diets. Collectively, these findings contributed to enriching the knowledge about the effects of HG diets on the structure and function of intestinal microbiota in ruminants.
Language	English
Publishing date	2022-06-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2606558-7
ISSN	2076-2615
ISSN	2076-2615
DOI	10.3390/ani12131695
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Relationship between TIM3 Expression on Peripheral T Lymphocytes and Post-Stroke Depression.

Mao, Qifen / Zhang, Peng / Qi, Weicui / Xia, Yueping / Chen, Tingting / Li, Xiaofang / Xu, Songquan / Zhong, Zhiqiang / Shangguan, Zuifei

Iranian journal of immunology : IJI

2023 Volume 20, Issue 4, Page(s) 427–437

Abstract: Background: T cell immunoglobulin and mucin domain-containing protein 3 (TIM3) is a regulatory molecule expressed on a variety of cell types, including CD3+ T cells. Few studies have been conducted to look into the correlation between TIM3 expression on ...

Abstract	Background: T cell immunoglobulin and mucin domain-containing protein 3 (TIM3) is a regulatory molecule expressed on a variety of cell types, including CD3+ T cells. Few studies have been conducted to look into the correlation between TIM3 expression on peripheral T lymphocytes and post-stroke depression (PSD). Objective: To investigate the relationship between TIM3 expressions on peripheral T lymphocytes in PSD patients. Methods: Acute stroke patients without depression (NPSD) (n=65), PSD patients (n=23), and body mass index (BMI), age, and education-matched healthy controls (HC) (n=59) were enrolled. Using flow cytometry, TIM3 expression was examined in the peripheral CD3+ CD4+ and CD3+ CD8+ T lymphocytes. Evaluation of the depressive severity in PSD patients was assessed using a 17-item Hamilton Depression Rating Scale (HAM-D-17). We used enzyme-linked immunosorbent assay (ELISA) to determine the serum concentrations of IL-1β, IL-6, IL-10, and IL-18. We further assessed the relationships between TIM3 expression, serum cytokine levels, and the HAM-D-17 scores. Results: CD3+ CD4+ T cells reduced significantly in PSD patients compared with the NPSD patients and HC. Both NPSD patients and PSD patients had a significant increase in TIM3 expression in their peripheral CD3+ CD4+ T lymphocytes, compared with HC. In PSD patients, a higher frequency of peripheral CD3+ CD8+ T lymphocytes showed significant expression of TIM3 compared to NPSD patients and HC. High TIM3 level on peripheral CD3+ CD8+ T lymphocytes was positively associated with the HAM-D score. Conclusion: Patients with PSD exhibit immune dysfunction. TIM3 might contribute to the development and severity of PSD, making it a potential therapeutic target.
MeSH term(s)	Humans ; CD8-Positive T-Lymphocytes ; Cytokines/metabolism ; Depression ; Hepatitis A Virus Cellular Receptor 2/metabolism
Chemical Substances	Cytokines ; Hepatitis A Virus Cellular Receptor 2 ; HAVCR2 protein, human
Language	English
Publishing date	2023-12-16
Publishing country	Iran
Document type	Journal Article
ZDB-ID	2616647-1
ISSN	1735-367X ; 1735-367X
ISSN (online)	1735-367X
ISSN	1735-367X
DOI	10.22034/iji.2023.98917.2598
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