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Article ; Online: A novel lateral flow immunoassay strip based on a label-free magnetic Fe

Du, Juan / Liu, Kai / Liu, Jialei / Zhao, Dianbo / Bai, Yanhong

Analytical methods : advancing methods and applications

2022 Volume 14, Issue 24, Page(s) 2423–2430

Abstract: ... Listeria ... ...

Abstract	Listeria monocytogenes
MeSH term(s)	Immunoassay/methods ; Listeria monocytogenes ; Magnetic Phenomena ; Metal-Organic Frameworks ; Nanocomposites ; Phthalic Acids
Chemical Substances	Metal-Organic Frameworks ; Phthalic Acids ; UiO-66
Language	English
Publishing date	2022-06-23
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2515210-5
ISSN	1759-9679 ; 1759-9660
ISSN (online)	1759-9679
ISSN	1759-9660
DOI	10.1039/d2ay00506a
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Joint Entity and Relation Extraction With Set Prediction Networks.

Sui, Dianbo / Zeng, Xiangrong / Chen, Yubo / Liu, Kang / Zhao, Jun

IEEE transactions on neural networks and learning systems

2023 Volume PP

Abstract: Joint entity and relation extraction is an important task in natural language processing, which aims to extract all relational triples mentioned in a given sentence. In essence, the relational triples mentioned in a sentence are in the form of a set, ... ...

Abstract	Joint entity and relation extraction is an important task in natural language processing, which aims to extract all relational triples mentioned in a given sentence. In essence, the relational triples mentioned in a sentence are in the form of a set, which has no intrinsic order between elements and exhibits the permutation invariant feature. However, previous seq2seq-based models require sorting the set of relational triples into a sequence beforehand with some heuristic global rules, which destroys the natural set structure. In order to break this bottleneck, we treat joint entity and relation extraction as a direct set prediction problem, so that the extraction model is not burdened with predicting the order of multiple triples. To solve this set prediction problem, we propose networks featured by transformers with non-autoregressive parallel decoding. In contrast to autoregressive approaches that generate triples one by one in a specific order, the proposed networks are able to directly output the final set of relational triples in one shot. Furthermore, we also design a set-based loss that forces unique predictions through bipartite matching. Compared with cross-entropy loss that highly penalizes small shifts in triple order, the proposed bipartite matching loss is invariant to any permutation of predictions; thus, it can provide the proposed networks with a more accurate training signal by ignoring triple order and focusing on relation types and entities. Various experiments on two benchmark datasets demonstrate that our proposed model significantly outperforms the current state-of-the-art (SoTA) models. Training code and trained models are now publicly available at http://github.com/DianboWork/SPN4RE.
Language	English
Publishing date	2023-04-17
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2023.3264735
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Confederated learning in healthcare: Training machine learning models using disconnected data separated by individual, data type and identity for Large-Scale health system Intelligence.

Liu, Dianbo / Fox, Kathe / Weber, Griffin / Miller, Tim

Journal of biomedical informatics

2022 Volume 134, Page(s) 104151

Abstract: Background: A patient's health information is generally fragmented across silos because it follows how care is delivered: multiple providers in multiple settings. Though it is technically feasible to reunite data for analysis in a manner that underpins ... ...

Abstract	Background: A patient's health information is generally fragmented across silos because it follows how care is delivered: multiple providers in multiple settings. Though it is technically feasible to reunite data for analysis in a manner that underpins a rapid learning healthcare system, privacy concerns and regulatory barriers limit data centralization for this purpose. Objectives: Machine learning can be conducted in a federated manner on patient datasets with the same set of variables but separated across storage. But federated learning cannot handle the situation where different data types for a given patient are separated vertically across different organizations and when patient ID matching across different institutions is difficult. We call methods that enable machine learning model training on data separated by two or more dimensions "confederated machine learning", which we aim to develop in this study. Methods: We propose and evaluate confederated learning for training machine learning models to stratify the risk of several diseases among silos when data are horizontally separated by individual, vertically separated by data type, and separated by identity without patient ID matching. The confederated learning method can be intuitively understood as a distributed learning method with representation learning, generative model, imputation method and data augmentation elements. Results: Our confederated learning method achieves AUCROC (Area Under The Curve Receiver Operating Characteristics) of 0.787 for diabetes prediction, 0.718 for psychological disorders prediction, and 0.698 for Ischemic heart disease prediction using nationwide health insurance claims. Conclusion: Our proposed confederated learning method successfully trained machine learning models on health insurance data separated by two or more dimensions.
MeSH term(s)	Delivery of Health Care ; Humans ; Intelligence ; Machine Learning ; Privacy ; ROC Curve
Language	English
Publishing date	2022-07-22
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2022.104151
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Article ; Online: Interaction mechanism of benzophenone-type UV filters on bovine serum albumin: Insights from structure-affinity relationship.

Liu, Hongrui / Ma, Yanxuan / Li, Xiang / Gu, Jiali / Dong, Dianbo

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering

2022 , Page(s) 1–10

Abstract: Benzophenone (BP)-type UV filters can cause structural changes of carrier protein in plasma. The binding process of five BP-type UV filters with bovine serum albumin (BSA) was investigated by multiple characterization methods, along with their structure- ... ...

Abstract	Benzophenone (BP)-type UV filters can cause structural changes of carrier protein in plasma. The binding process of five BP-type UV filters with bovine serum albumin (BSA) was investigated by multiple characterization methods, along with their structure-affinity relationship involving the structure of the five BP-type UV filters and their binding affinity for BSA. The BP-type UV filters investigated bound to BSA spontaneously, and altered conformation of BSA. The binding constants and number of binding sites between BP-type UV filters and BSA were 10
Language	English
Publishing date	2022-11-23
Publishing country	England
Document type	Journal Article
ZDB-ID	196584-0
ISSN	1532-4117 ; 0360-1226 ; 1077-1204 ; 1093-4529
ISSN (online)	1532-4117
ISSN	0360-1226 ; 1077-1204 ; 1093-4529
DOI	10.1080/10934529.2022.2148992
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Development of a fluorescent test strip sensor based on surface positively-charged magnetic bead separation for the detection of

Du, Juan / Liu, Jialei / Liu, Kai / Zhao, Dianbo / Sagratini, Gianni / Tao, Jing / Bai, Yanhong

Analytical methods : advancing methods and applications

2022 Volume 14, Issue 22, Page(s) 2188–2194

Abstract: ... Listeria ... ...

Abstract	Listeria monocytogenes
MeSH term(s)	Animals ; Food Microbiology ; Immunomagnetic Separation/methods ; Listeria monocytogenes/genetics ; Magnetic Phenomena ; Polymerase Chain Reaction
Language	English
Publishing date	2022-06-09
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2515210-5
ISSN	1759-9679 ; 1759-9660
ISSN (online)	1759-9679
ISSN	1759-9660
DOI	10.1039/d2ay00384h
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries

Ikram, Zarif / Pan, Ling / Liu, Dianbo

2023

Abstract: Due to limited resources and fast economic growth, designing optimal transportation road networks with traffic simulation and validation in a cost-effective manner is vital for developing countries, where extensive manual testing is expensive and often ... ...

Abstract	Due to limited resources and fast economic growth, designing optimal transportation road networks with traffic simulation and validation in a cost-effective manner is vital for developing countries, where extensive manual testing is expensive and often infeasible. Current rule-based road design generators lack diversity, a key feature for design robustness. Generative Flow Networks (GFlowNets) learn stochastic policies to sample from an unnormalized reward distribution, thus generating high-quality solutions while preserving their diversity. In this work, we formulate the problem of linking incident roads to the circular junction of a roundabout by a Markov decision process, and we leverage GFlowNets as the Junction-Art road generator. We compare our method with related methods and our empirical results show that our method achieves better diversity while preserving a high validity score. Comment: 6 pages. Submitted to ReALML@NeurIPS (2023)
Keywords	Computer Science - Artificial Intelligence ; Computer Science - Robotics
Subject code	006
Publishing date	2023-10-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Enhancing Human Capabilities through Symbiotic Artificial Intelligence with Shared Sensory Experiences

Hao, Rui / Liu, Dianbo / Hu, Linmei

2023

Abstract: The merging of human intelligence and artificial intelligence has long been a subject of interest in both science fiction and academia. In this paper, we introduce a novel concept in Human-AI interaction called Symbiotic Artificial Intelligence with ... ...

Abstract	The merging of human intelligence and artificial intelligence has long been a subject of interest in both science fiction and academia. In this paper, we introduce a novel concept in Human-AI interaction called Symbiotic Artificial Intelligence with Shared Sensory Experiences (SAISSE), which aims to establish a mutually beneficial relationship between AI systems and human users through shared sensory experiences. By integrating multiple sensory input channels and processing human experiences, SAISSE fosters a strong human-AI bond, enabling AI systems to learn from and adapt to individual users, providing personalized support, assistance, and enhancement. Furthermore, we discuss the incorporation of memory storage units for long-term growth and development of both the AI system and its human user. As we address user privacy and ethical guidelines for responsible AI-human symbiosis, we also explore potential biases and inequalities in AI-human symbiosis and propose strategies to mitigate these challenges. Our research aims to provide a comprehensive understanding of the SAISSE concept and its potential to effectively support and enhance individual human users through symbiotic AI systems. This position article aims at discussing poteintial AI-human interaction related topics within the scientific community, rather than providing experimental or theoretical results.
Keywords	Computer Science - Human-Computer Interaction ; Computer Science - Artificial Intelligence
Subject code	004 ; 170
Publishing date	2023-05-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Two-stage Federated Phenotyping and Patient Representation Learning.

Liu, Dianbo / Dligach, Dmitriy / Miller, Timothy

Proceedings of the conference. Association for Computational Linguistics. Meeting

2021 Volume 2019, Page(s) 283–291

Abstract: A large percentage of medical information is in unstructured text format in electronic medical record systems. Manual extraction of information from clinical notes is extremely time consuming. Natural language processing has been widely used in recent ... ...

Abstract	A large percentage of medical information is in unstructured text format in electronic medical record systems. Manual extraction of information from clinical notes is extremely time consuming. Natural language processing has been widely used in recent years for automatic information extraction from medical texts. However, algorithms trained on data from a single healthcare provider are not generalizable and error-prone due to the heterogeneity and uniqueness of medical documents. We develop a two-stage federated natural language processing method that enables utilization of clinical notes from different hospitals or clinics without moving the data, and demonstrate its performance using obesity and comorbities phenotyping as medical task. This approach not only improves the quality of a specific clinical task but also facilitates knowledge progression in the whole healthcare system, which is an essential part of learning health system. To the best of our knowledge, this is the first application of federated machine learning in clinical NLP.
Language	English
Publishing date	2021-04-10
Publishing country	United States
Document type	Journal Article
ISSN	0736-587X
ISSN	0736-587X
DOI	10.18653/v1/W19-5030
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Effect of Chickpea Dietary Fiber on the Emulsion Gel Properties of Pork Myofibrillar Protein.

Zhao, Dianbo / Yan, Shuliang / Liu, Jialei / Jiang, Xi / Li, Junguang / Wang, Yuntao / Zhao, Jiansheng / Bai, Yanhong

Foods (Basel, Switzerland)

2023 Volume 12, Issue 13

Abstract: In this study, the effect of chickpea dietary fiber (CDF) concentration (0%, 0.4%, 0.8%, 1.2%, 1.6%, and 2.0%) on emulsion gel properties of myofibrillar protein (MP) was investigated. It was found that the emulsifying activity index (EAI) and ... ...

Abstract	In this study, the effect of chickpea dietary fiber (CDF) concentration (0%, 0.4%, 0.8%, 1.2%, 1.6%, and 2.0%) on emulsion gel properties of myofibrillar protein (MP) was investigated. It was found that the emulsifying activity index (EAI) and emulsifying stability index (ESI) of MP increased with the increasing content of CDF. Moreover, the water- and fat-binding capacity (WFB), gel strength, storage modulus (G'), and loss modulus (G") of MP emulsion gel also increased with increasing content of CDF. When the concentration of CDF was 2%, the most significant improvement was observed for EAI, breaking force, and WFB (
Language	English
Publishing date	2023-07-04
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2704223-6
ISSN	2304-8158
ISSN	2304-8158
DOI	10.3390/foods12132597
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Using Artificial Neural Network Condensation to Facilitate Adaptation of Machine Learning in Medical Settings by Reducing Computational Burden: Model Design and Evaluation Study.

Liu, Dianbo / Zheng, Ming / Sepulveda, Nestor Andres

JMIR formative research

2021 Volume 5, Issue 12, Page(s) e20767

Abstract: Background: Machine learning applications in the health care domain can have a great impact on people's lives. At the same time, medical data is usually big, requiring a significant number of computational resources. Although this might not be a problem ...

Abstract	Background: Machine learning applications in the health care domain can have a great impact on people's lives. At the same time, medical data is usually big, requiring a significant number of computational resources. Although this might not be a problem for the wide adoption of machine learning tools in high-income countries, the availability of computational resources can be limited in low-income countries and on mobile devices. This can limit many people from benefiting from the advancement in machine learning applications in the field of health care. Objective: In this study, we explore three methods to increase the computational efficiency and reduce model sizes of either recurrent neural networks (RNNs) or feedforward deep neural networks (DNNs) without compromising their accuracy. Methods: We used inpatient mortality prediction as our case analysis upon review of an intensive care unit dataset. We reduced the size of RNN and DNN by applying pruning of "unused" neurons. Additionally, we modified the RNN structure by adding a hidden layer to the RNN cell but reducing the total number of recurrent layers to accomplish a reduction of the total parameters used in the network. Finally, we implemented quantization on DNN by forcing the weights to be 8 bits instead of 32 bits. Results: We found that all methods increased implementation efficiency, including training speed, memory size, and inference speed, without reducing the accuracy of mortality prediction. Conclusions: Our findings suggest that neural network condensation allows for the implementation of sophisticated neural network algorithms on devices with lower computational resources.
Language	English
Publishing date	2021-12-08
Publishing country	Canada
Document type	Journal Article
ISSN	2561-326X
ISSN (online)	2561-326X
DOI	10.2196/20767
Database	MEDical Literature Analysis and Retrieval System OnLINE

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