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Article ; Online: CLART

Yinlong Xiao / Zongcheng Ji / Jianqiang Li / Qing Zhu

Heliyon, Vol 9, Iss 10, Pp e20692- (2023)

A cascaded lattice-and-radical transformer network for Chinese medical named entity recognition

2023

Abstract: Chinese medical named entity recognition (NER) is a fundamental task in Chinese medical natural language processing, aiming to recognize Chinese medical entities within unstructured medical texts. However, it poses significant challenges mainly due to ... ...

Abstract	Chinese medical named entity recognition (NER) is a fundamental task in Chinese medical natural language processing, aiming to recognize Chinese medical entities within unstructured medical texts. However, it poses significant challenges mainly due to the extensive usage of medical terms in Chinese medical texts. Although previous studies have made attempts to incorporate lexical or radical knowledge in order to improve the comprehension of medical texts, these studies either focus solely on one of these aspects or utilize a basic concatenation operation to combine these features, which fails to fully utilize the potential of lexical and radical knowledge. In this paper, we propose a novel Cascaded LAttice-and-Radical Transformer (CLART) network to exploit both lexical and radical information for Chinese medical NER. Specifically, given a sentence, a medical lexicon, and a radical dictionary, we first construct a flat lattice (i.e., character-word sequence) for the sentence and radical components of each Chinese character through word matching and radical parsing, respectively. We then employ a lattice Transformer module to capture the dense interactions between characters and matched words, facilitating the enhanced utilization of lexical knowledge. Subsequently, we design a radical Transformer module to model the dense interactions between the lattice and radical features, facilitating better fusion of the lexical and radical knowledge. Finally, we feed the updated lattice-and-radical-aware character representations into a Conditional Random Fields (CRF) decoder to obtain the predicted labels. Experimental results conducted on two publicly available Chinese medical NER datasets show the effectiveness of the proposed method.
Keywords	Chinese medical named entity recognition ; Lattice structure ; Radical information ; Attention mechanism ; Transformer ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
Subject code	410
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms

Zitong Wang / Yan Pei / Jianqiang Li

Applied Sciences, Vol 13, Iss 4643, p

2023 Volume 4643

Abstract: The multi-objective optimization problem is difficult to solve with conventional optimization methods and algorithms because there are conflicts among several optimization objectives and functions. Through the efforts of researchers and experts from ... ...

Abstract	The multi-objective optimization problem is difficult to solve with conventional optimization methods and algorithms because there are conflicts among several optimization objectives and functions. Through the efforts of researchers and experts from different fields for the last 30 years, the research and application of multi-objective evolutionary algorithms (MOEA) have made excellent progress in solving such problems. MOEA has become one of the primary used methods and technologies in the realm of multi-objective optimization. It is also a hotspot in the evolutionary computation research community. This survey provides a comprehensive investigation of MOEA algorithms that have emerged in recent decades and summarizes and classifies the classical MOEAs by evolutionary mechanism from the viewpoint of the search strategy. This paper divides them into three categories considering the search strategy of MOEA, i.e., decomposition-based MOEA algorithms, dominant relation-based MOEA algorithms, and evaluation index-based MOEA algorithms. This paper selects the relevant representative algorithms for a detailed summary and analysis. As a prospective research direction, we propose to combine the chaotic evolution algorithm with these representative search strategies for improving the search capability of multi-objective optimization algorithms. The capability of the new multi-objective evolutionary algorithm has been discussed, which further proposes the future research direction of MOEA. It also lays a foundation for the application and development of MOEA with these prospective works in the future.
Keywords	multi-objective evolutionary computation ; multi-objective optimization problem ; search strategy ; optimization ; meta-heuristics ; 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	2023-04-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Sex-specific selective effect of winter weather on morphological traits in a small passerine bird

Yue Wang / Qian Hu / Jiliang Xu / Jianqiang Li

Avian Research, Vol 14, Iss , Pp 100093- (2023)

2023

Abstract: Harsh environmental conditions often impose strong selection on the phenotype of natural populations through impacts on their fitness. For overwintering birds, winter is an important period for survival because the weather conditions in winter is usually ...

Abstract	Harsh environmental conditions often impose strong selection on the phenotype of natural populations through impacts on their fitness. For overwintering birds, winter is an important period for survival because the weather conditions in winter is usually severer than in other seasons and birds often suffer more thermoregulation costs while food is in short supply. Thus, the selective effect of weather conditions on phenotype in winter is expected to be strong. In this study, we examined the relationship of overwinter survival of Silver-throated Tits (Aegithalos glaucogularis) with their morphological traits under different levels of winter conditions (winters with and without snowstorms) to explore the differential selective effect of winter weather on their morphology. We found that regardless of whether the winter experienced a snowstorm, the female Silver-throated Tits with a smaller bill surface area or smaller bill depth and a smaller surface area:volume ratio were more likely to survive during the winter, which supported the hypothesis of selection for heat retention. Furthermore, the females with a smaller body length survived better than the larger females, indicating that the lesser food requirements for smaller body sizes may confer advantages during the winter when food availability was reduced. In addition, in agreement with the finding in many short-lived birds that survival rate increases with the increase of age, older (≥2-year-old) female Silver-throated Tits had higher overwinter survival than 1-year-old females. However, we did not find any correlation of morphological traits and age with survival in male Silver-throated Tits. These results illustrate differential selective effects of winter weather on female and male Silver-throated Tits and contribute to the knowledge of sex-specific selection on the phenotype of natural populations.
Keywords	Aegithalos glaucogularis ; Age ; Morphological traits ; Natural selection ; Overwinter survival ; Sex-specific selection ; Zoology ; QL1-991
Subject code	590
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	KeAi Communications Co., Ltd.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Effect of digital economy on large-scale pig farming

Kun Zhou / Huan Wang / Jin Wu / Jianqiang Li

Cogent Food & Agriculture, Vol 9, Iss

An empirical study from China

2023 Volume 1

Abstract: AbstractLarge-scale farming is an important way for sustainable development of pig industry. In this regard, the digital economy has an important influence on the industrial structure and economic development, but whether it is conducive to large-scale ... ...

Abstract	AbstractLarge-scale farming is an important way for sustainable development of pig industry. In this regard, the digital economy has an important influence on the industrial structure and economic development, but whether it is conducive to large-scale pig farming (LSPF) needs further study. This study assesses the development level of the digital economy in 31 provinces of China and examines the transmission mechanism of the digital economy in LSPF. The results demonstrate that the digital economy can contribute both directly and indirectly to the LSPF through improved technological innovation, enhanced merchandise sales, and reduced labor costs of raising pigs. More importantly, the digital economy has a significant negative spatial spillover effect on LSPF; that is, it is not conducive to large-scale farming in neighboring localities. The results of the heterogeneity analysis further show that the digital economy is more significant for regions that have been identified as LSPF key and potential growth areas. The findings provide a reference for policymakers and professionals in emerging economies to facilitate the scale-up and sustainability of the pig industry.
Keywords	digital economy ; large-scale pig farming ; spatial effect ; mediating effect ; regional diversity ; Agriculture ; S ; Food processing and manufacture ; TP368-456
Subject code	910
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: DeepCAC

Jidong Zhang / Bo Liu / Jiahui Wu / Zhihan Wang / Jianqiang Li

BMC Bioinformatics, Vol 24, Iss 1, Pp 1-

a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network

2023 Volume 15

Abstract: Abstract Understanding gene expression processes necessitates the accurate classification and identification of transcription factors, which is supported by high-throughput sequencing technologies. However, these techniques suffer from inherent ... ...

Abstract	Abstract Understanding gene expression processes necessitates the accurate classification and identification of transcription factors, which is supported by high-throughput sequencing technologies. However, these techniques suffer from inherent limitations such as time consumption and high costs. To address these challenges, the field of bioinformatics has increasingly turned to deep learning technologies for analyzing gene sequences. Nevertheless, the pursuit of improved experimental results has led to the inclusion of numerous complex analysis function modules, resulting in models with a growing number of parameters. To overcome these limitations, it is proposed a novel approach for analyzing DNA transcription factor sequences, which is named as DeepCAC. This method leverages deep convolutional neural networks with a multi-head self-attention mechanism. By employing convolutional neural networks, it can effectively capture local hidden features in the sequences. Simultaneously, the multi-head self-attention mechanism enhances the identification of hidden features with long-distant dependencies. This approach reduces the overall number of parameters in the model while harnessing the computational power of sequence data from multi-head self-attention. Through training with labeled data, experiments demonstrate that this approach significantly improves performance while requiring fewer parameters compared to existing methods. Additionally, the effectiveness of our approach is validated in accurately predicting DNA transcription factor sequences.
Keywords	Bioinformatics ; Attention mechanism ; DNA transcription factors sequence ; Convolutional neural networks ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	612
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: DL-PPI

Jiahui Wu / Bo Liu / Jidong Zhang / Zhihan Wang / Jianqiang Li

BMC Bioinformatics, Vol 24, Iss 1, Pp 1-

a method on prediction of sequenced protein–protein interaction based on deep learning

2023 Volume 21

Abstract: Abstract Purpose Sequenced Protein–Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic ... ...

Abstract	Abstract Purpose Sequenced Protein–Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets. Results In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction.
Keywords	Protein–protein interaction ; Deep learning ; Graph neural network ; Feature extraction ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	006
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Efficacy of Bevacizumab and Gemcitabine in Combination with Cisplatin in the Treatment of Esophageal Cancer and the Effect on the Incidence of Adverse Reactions

Jiangfeng Wang / Qiang Zhao / Lei Cai / Jianqiang Li / Sheng Chen

BioMed Research International, Vol

2022 Volume 2022

Abstract: Objective. To evaluate the efficacy of bevacizumab and gemcitabine in combination with cisplatin in the treatment of esophageal cancer and the effect on the incidence of adverse reactions. Methods. A total of 100 esophageal cancer patients admitted to ... ...

Abstract	Objective. To evaluate the efficacy of bevacizumab and gemcitabine in combination with cisplatin in the treatment of esophageal cancer and the effect on the incidence of adverse reactions. Methods. A total of 100 esophageal cancer patients admitted to our hospital from March 2019 to March 2021 were identified as research subjects and randomized into the control group and the study group, with 50 cases in each group. The control group was treated with gemcitabine combined with cisplatin, and the study group was treated with the triple therapy of bevacizumab, gemcitabine, and cisplatin. The treatment efficiency and the incidence of adverse reactions were compared between the two groups of patients. Results. The total treatment efficiency in the study group was 86%, which was significantly higher than that of 66% in the control group (P<0.05). After treatment, the levels of vascular endothelial growth factor (VEGF), Cyfra21-1, and C-met were reduced in both groups, with significantly lower levels in the study group than in the control group (P<0.05). The incidence of all CTCAE, ototoxicity, and nephrotoxicity was comparable between the two groups (P>0.05). The survival rates of patients in the study group were 88% and 54% at 1 and 2 years after treatment, which were significantly higher than that of 68% and 32% in the control group (P<0.05). Conclusion. The clinical efficiency of bevacizumab and gemcitabine combined with cisplatin in the treatment of esophageal cancer is remarkable, which improves the survival of patients, and is worthy of clinical promotion and application.
Keywords	Medicine ; R
Subject code	616
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: An Automated In-Depth Feature Learning Algorithm for Breast Abnormality Prognosis and Robust Characterization from Mammography Images Using Deep Transfer Learning

Tariq Mahmood / Jianqiang Li / Yan Pei / Faheem Akhtar

Biology, Vol 10, Iss 859, p

2021 Volume 859

Abstract: Background: Diagnosing breast cancer masses and calcification clusters have paramount significance in mammography, which aids in mitigating the disease’s complexities and curing it at early stages. However, a wrong mammogram interpretation may lead to an ...

Abstract	Background: Diagnosing breast cancer masses and calcification clusters have paramount significance in mammography, which aids in mitigating the disease’s complexities and curing it at early stages. However, a wrong mammogram interpretation may lead to an unnecessary biopsy of the false-positive findings, which reduces the patient’s survival chances. Consequently, approaches that learn to discern breast masses can reduce the number of misconceptions and incorrect diagnoses. Conventionally used classification models focus on feature extraction techniques specific to a particular problem based on domain information. Deep learning strategies are becoming promising alternatives to solve the many challenges of feature-based approaches. Methods: This study introduces a convolutional neural network (ConvNet)-based deep learning method to extract features at varying densities and discern mammography’s normal and suspected regions. Two different experiments were carried out to make an accurate diagnosis and classification. The first experiment consisted of five end-to-end pre-trained and fine-tuned deep convolution neural networks (DCNN). The in-depth features extracted from the ConvNet are also used to train the support vector machine algorithm to achieve excellent performance in the second experiment. Additionally, DCNN is the most frequently used image interpretation and classification method, including VGGNet, GoogLeNet, MobileNet, ResNet, and DenseNet. Moreover, this study pertains to data cleaning, preprocessing, and data augmentation, and improving mass recognition accuracy. The efficacy of all models is evaluated by training and testing three mammography datasets and has exhibited remarkable results. Results: Our deep learning ConvNet+SVM model obtained a discriminative training accuracy of 97.7% and validating accuracy of 97.8%, contrary to this, VGGNet16 method yielded 90.2%, 93.5% for VGGNet19, 63.4% for GoogLeNet, 82.9% for MobileNetV2, 75.1% for ResNet50, and 72.9% for DenseNet121. Conclusions: The proposed ...
Keywords	breast cancer mass ; deep learning ; mammography classification ; deep transfer learning ; augmentation ; computer-aided diagnosis ; Biology (General) ; QH301-705.5
Subject code	006
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Environmental Regulation, Rural Residents’ Health Investment, and Agricultural Eco-Efficiency

Kun Zhou / Xingqiang Zheng / Yan Long / Jin Wu / Jianqiang Li

International Journal of Environmental Research and Public Health, Vol 19, Iss 3125, p

An Empirical Analysis Based on 31 Chinese Provinces

2022 Volume 3125

Abstract: This paper explores the effects of environmental regulation ( ER ) and rural residents’ health investment (RRHI) on agricultural eco-efficiency ( AEE ) to provide a reference for the Chinese Government and other developing countries for implementing ... ...

Abstract	This paper explores the effects of environmental regulation ( ER ) and rural residents’ health investment (RRHI) on agricultural eco-efficiency ( AEE ) to provide a reference for the Chinese Government and other developing countries for implementing environmental regulation policies and to provide new paths to further improve green development in agriculture. Using the panel data of 31 Chinese provinces from 2009–2018, the Super-SBM model was used to measure AEE . The role of ER on AEE was analyzed based on panel two-way fixed effects with endogeneity treatment and a robustness test, and this mediating effect analysis was conducted to analyze the role of RRHI in ER and AEE , examining the extent of the effect of ER on AEE in three regions of China—eastern, central and western—using a heterogeneity analysis. The results of the study show that: (1) from a national perspective, ER has a significant positive impact on AEE , showing that ER is effective at this stage; (2) when RRHI is used as a mediating variable, the rising ER ’s intensity can promote AEE by increasing RRHI; and (3) the results of the heterogeneity analysis show that ER has the greatest impact on AEE in the economically developed eastern region; the western region with a weaker level of economic development is in second place. However, ER has a negative impact on AEE in the central region with a medium level of economic development. Thus, the impact of ER on AEE will show great differences depending on the stage of economic development.
Keywords	ER ; AEE ; RRHI ; mediating effects ; heterogeneity analysis ; Medicine ; R
Subject code	306
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Dynamic learning for imbalanced data in learning chest X-ray and CT images

Saeed Iqbal / Adnan N. Qureshi / Jianqiang Li / Imran Arshad Choudhry / Tariq Mahmood

Heliyon, Vol 9, Iss 6, Pp e16807- (2023)

2023

Abstract: Massive annotated datasets are necessary for networks of deep learning. When a topic is being researched for the first time, as in the situation of the viral epidemic, handling it with limited annotated datasets might be difficult. Additionally, the ... ...

Abstract	Massive annotated datasets are necessary for networks of deep learning. When a topic is being researched for the first time, as in the situation of the viral epidemic, handling it with limited annotated datasets might be difficult. Additionally, the datasets are quite unbalanced in this situation, with limited findings coming from significant instances of the novel illness. We offer a technique that allows a class balancing algorithm to understand and detect lung disease signs from chest X-ray and CT images. Deep learning techniques are used to train and evaluate images, enabling the extraction of basic visual attributes. The training objects' characteristics, instances, categories, and relative data modeling are all represented probabilistically. It is possible to identify a minority category in the classification process by using an imbalance-based sample analyzer. In order to address the imbalance problem, learning samples from the minority class are examined. The Support Vector Machine (SVM) is used to categorize images in clustering. Physicians and medical professionals can use the CNN model to validate their initial assessments of malignant and benign categorization. The proposed technique for class imbalance (3-Phase Dynamic Learning (3PDL)) and parallel CNN model (Hybrid Feature Fusion (HFF)) for multiple modalities achieve a high F1 score of 96.83 and precision is 96.87, its outstanding accuracy and generalization suggest that it may be utilized to create a pathologist's help tool.
Keywords	Class imbalance ; Random sampling ; Dynamic learning ; Feature fusion ; Ensemble learning ; Convolutional neural network ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
Subject code	006
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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