A Novel Deep Learning Framework for Internal Gross Target Volume Definition from 4D Computed Tomography of Lung Cancer Patients

In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from four-dimensional computed tomography (4DCT), which is applied to patients with lung cancer treated by Stereotactic Body Radiation Therapy (SBRT). 77 patients who underwent SBRT followed by 4DCT scans were incorporated in a retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework, for the purpose of comparison, three other IGTVs base on common methods was also delineated, we compared the relative volume difference (RVI), matching index (MI) and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis assesses the tumor volume and motion range as clinical influencing factors in the MI variation. Experimental results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 BMP, the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential candidate for an optimal combination to synthesis IGTV in all respiration amplitudes

How to Optimize the Supply and Allocation of Medical Emergency Resources During Public Health Emergencies

The solutions to the supply and allocation of medical emergency resources during public health emergencies greatly affect the efficiency of epidemic prevention and control. Currently, the main problem in computational epidemiology is how the allocation scheme should be adjusted in accordance with epidemic trends to satisfy the needs of population coverage, epidemic propagation prevention, and the social allocation balance. More specifically, the metropolitan demand for medical emergency resources varies depending on different local epidemic situations. It is therefore difficult to satisfy all objectives at the same time in real applications. In this paper, a data-driven multi-objective optimization method, called as GA-PSO, is proposed to address such problem. It adopts the one-way crossover and mutation operations to modify the particle updating framework in order to escape the local optimum. Taking the megacity Shenzhen in China as an example, experiments show that GA-PSO effectively balances different objectives and generates a feasible allocation strategy. Such a strategy does not only support the decision-making process of the Shenzhen center in terms of disease control and prevention, but it also enables us to control the potential propagation of COVID-19 and other epidemics. © Copyright © 2020 Wang, Deng, Yuan, Zhang, Zhang, Cai, Gao and Kurths

Alleviating Representational Shift for Continual Fine-tuning

We study a practical setting of continual learning: fine-tuning on a pre-trained model continually. Previous work has found that, when training on new tasks, the features (penultimate layer representations) of previous data will change, called representational shift. Besides the shift of features, we reveal that the intermediate layers' representational shift (IRS) also matters since it disrupts batch normalization, which is another crucial cause of catastrophic forgetting. Motivated by this, we propose ConFiT, a fine-tuning method incorporating two components, cross-convolution batch normalization (Xconv BN) and hierarchical fine-tuning. Xconv BN maintains pre-convolution running means instead of post-convolution, and recovers post-convolution ones before testing, which corrects the inaccurate estimates of means under IRS. Hierarchical fine-tuning leverages a multi-stage strategy to fine-tune the pre-trained network, preventing massive changes in Conv layers and thus alleviating IRS. Experimental results on four datasets show that our method remarkably outperforms several state-of-the-art methods with lower storage overhead

Global Research Trends of Artificial Intelligence on Histopathological Images: A 20-Year Bibliometric Analysis

Cancer has become a major threat to global health care. With the development of computer science, artificial intelligence (AI) has been widely applied in histopathological images (HI) analysis. This study analyzed the publications of AI in HI from 2001 to 2021 by bibliometrics, exploring the research status and the potential popular directions in the future. A total of 2844 publications from the Web of Science Core Collection were included in the bibliometric analysis. The country/region, institution, author, journal, keyword, and references were analyzed by using VOSviewer and CiteSpace. The results showed that the number of publications has grown rapidly in the last five years. The USA is the most productive and influential country with 937 publications and 23,010 citations, and most of the authors and institutions with higher numbers of publications and citations are from the USA. Keyword analysis showed that breast cancer, prostate cancer, colorectal cancer, and lung cancer are the tumor types of greatest concern. Co-citation analysis showed that classification and nucleus segmentation are the main research directions of AI-based HI studies. Transfer learning and self-supervised learning in HI is on the rise. This study performed the first bibliometric analysis of AI in HI from multiple indicators, providing insights for researchers to identify key cancer types and understand the research trends of AI application in HI

Bypassing Logits Bias in Online Class-Incremental Learning with a Generative Framework

Continual learning requires the model to maintain the learned knowledge while learning from a non-i.i.d data stream continually. Due to the single-pass training setting, online continual learning is very challenging, but it is closer to the real-world scenarios where quick adaptation to new data is appealing. In this paper, we focus on online class-incremental learning setting in which new classes emerge over time. Almost all existing methods are replay-based with a softmax classifier. However, the inherent logits bias problem in the softmax classifier is a main cause of catastrophic forgetting while existing solutions are not applicable for online settings. To bypass this problem, we abandon the softmax classifier and propose a novel generative framework based on the feature space. In our framework, a generative classifier which utilizes replay memory is used for inference, and the training objective is a pair-based metric learning loss which is proven theoretically to optimize the feature space in a generative way. In order to improve the ability to learn new data, we further propose a hybrid of generative and discriminative loss to train the model. Extensive experiments on several benchmarks, including newly introduced task-free datasets, show that our method beats a series of state-of-the-art replay-based methods with discriminative classifiers, and reduces catastrophic forgetting consistently with a remarkable margin

Evaluation and optimization of analytical procedure and sample preparation for polar Streptomyces albus J1074 metabolome profiling

Metabolomics is an essential discipline in omics technology that promotes research on the biology of microbial systems. Streptomyces albus J1074 is a model organism used in fundamental research and industrial microbiology. Nevertheless, a comprehensive and standardized method for analyzing the metabolome of S. albus J1074 is yet to be developed. Thus, we comprehensively evaluated and optimized the analytical procedure and sample preparation for profiling polar metabolites using hydrophilic interaction liquid chromatography (HILIC) coupled with high-resolution mass spectrometry (HRMS). We systematically examined the HILIC columns, quenching solutions, sample-to-quenching ratios, and extraction methods. Then, the optimal protocol was used to investigate the dynamic intracellular polar metabolite profile of the engineered S. albus J1074 strains during spinosad (spinosyn A and spinosyn D) fermentation. A total of 3648 compounds were detected, and 83 metabolites were matched to the standards. The intracellular metabolomic profiles of engineered S. albus J1074 strains (ADE-AP and OE3) were detected; furthermore, their metabolomes in different stages were analyzed to reveal the reasons for their differences in their spinosad production, as well as the current metabolic limitation of heterologous spinosad production in S. albus J1074. The HILIC-HRMS method is a valuable tool for investigating polar metabolomes, and provides a reference methodology to study other Streptomyces metabolomes

Dual-Alignment Pre-training for Cross-lingual Sentence Embedding

Recent studies have shown that dual encoder models trained with the sentence-level translation ranking task are effective methods for cross-lingual sentence embedding. However, our research indicates that token-level alignment is also crucial in multilingual scenarios, which has not been fully explored previously. Based on our findings, we propose a dual-alignment pre-training (DAP) framework for cross-lingual sentence embedding that incorporates both sentence-level and token-level alignment. To achieve this, we introduce a novel representation translation learning (RTL) task, where the model learns to use one-side contextualized token representation to reconstruct its translation counterpart. This reconstruction objective encourages the model to embed translation information into the token representation. Compared to other token-level alignment methods such as translation language modeling, RTL is more suitable for dual encoder architectures and is computationally efficient. Extensive experiments on three sentence-level cross-lingual benchmarks demonstrate that our approach can significantly improve sentence embedding. Our code is available at https://github.com/ChillingDream/DAP.Comment: ACL 202

Prediction of Plasticizer Property Based on an Improved Genetic Algorithm

Different plasticizers have obvious differences in plasticizing properties. As one of the important indicators for evaluating plasticization performance, the substitution factor (SF) has great significance for product cost accounting. In this research, a genetic algorithm with “variable mutation probability” was developed to screen the key molecular descriptors of plasticizers that are highly correlated with the SF, and a SF prediction model was established based on these filtered molecular descriptors. The results show that the improved genetic algorithm greatly improved the prediction accuracy in different regression models. The coefficient of determination (R2) for the test set and the cross-validation both reached 0.92, which is at least 0.15 higher than the R2 of the unimproved genetic algorithm. From the results of the selected descriptors, most of the descriptors focused on describing the branching of the molecule, which is consistent with the view that the branching chain plays an important role in the plasticization process. As the first study to establish the relationship between plasticizer SF and plasticizer molecular structure, this work provides a basis for subsequent plasticizer performance and evaluation system modeling

Oligogenic basis of premature ovarian insufficiency: an observational study

Abstract Background The etiology of premature ovarian insufficiency, that is, the loss of ovarian activity before 40 years of age, is complex. Studies suggest that genetic factors are involved in 20–25% of cases. The aim of this study was to explore the oligogenic basis of premature ovarian insufficiency. Results Whole-exome sequencing of 93 patients with POI and whole-genome sequencing of 465 controls were performed. In the gene-burden analysis, multiple genetic variants, including those associated with DNA damage repair and meiosis, were more common in participants with premature ovarian insufficiency than in controls. The ORVAL-platform analysis confirmed the pathogenicity of the RAD52 and MSH6 combination. Conclusions The results of this study indicate that oligogenic inheritance is an important cause of premature ovarian insufficiency and provide insights into the biological mechanisms underlying premature ovarian insufficiency