Dynamics for the diffusive Leslie-Gower model with double free boundaries

In this paper we investigate a free boundary problem for the diffusive Leslie-Gower prey-predator model with double free boundaries in one space dimension. This system models the expanding of an invasive or new predator species in which the free boundaries represent expanding fronts of the predator species. We first prove the existence, uniqueness and regularity of global solution. Then provide a spreading-vanishing dichotomy, namely the predator species either successfully spreads to infinity as $t\to\infty$ at both fronts and survives in the new environment, or it spreads within a bounded area and dies out in the long run. The long time behavior of $(u,v)$ and criteria for spreading and vanishing are also obtained. Because the term $v/u$ (which appears in the second equation) may be unbounded when $u$ nears zero, it will bring some difficulties for our study.Comment: 19 page

The nonlocal dispersal equation with seasonal succession

In this paper, we focus on the nonlocal dispersal monostable equation with seasonal succession, which can be used to describe the dynamics of species in an environment alternating between bad and good seasons. We first prove the existence and uniqueness of global positive solution, and then discuss the long time behaviors of solution. It is shown that its dynamics is completely determined by the sign of the principal eigenvalue, i.e., the time periodic problem has no positive solution and the solution of the initial value problem tends to zero when principal eigenvalue is non-negative, while the time periodic positive solution exists uniquely and is globally asymptotically stable when principal eigenvalue is negative.Comment: 17 page

Preclinical risk of bias assessment and PICO extraction using natural language processing

Drug development starts with preclinical studies which test the efficacy and toxicology of potential candidates in living animals, before proceeding to clinical trials examined on human subjects. Many drugs shown to be effective in preclinical animal studies fail in clinical trials, indicating the potential reproducibility issues and translation failure. To obtain less biased research findings, systematic reviews are performed to collate all relevant evidence from publications. However, systematic reviews are time-consuming and researchers have advocated the use of automation techniques to speed the process and reduce human efforts. Good progress has been made in implementing automation tools into reviews for clinical trials while the tools developed for preclinical systematic reviews are scarce. Tools for preclinical systematic reviews should be designed specifically because preclinical experiments differ from clinical trials. In this thesis, I explore natural language processing models for facilitating two stages in preclinical systematic reviews: risk of bias assessment and PICO extraction. There are a range of measures used to reduce bias in animal experiments and many checklist criteria require the reporting of those measures in publications. In the first part of the thesis, I implement several binary classification models to indicate the reporting of random allocation to groups, blinded assessment of outcome, conflict of interests, compliance of animal welfare regulations, and statement of animal exclusions in preclinical publications. I compare traditional machine learning classifiers with several text representation methods, convolutional/recurrent/hierarchical neural networks, and propose two strategies to adapt BERT models to long documents. My findings indicate that neural networks and BERT-based models achieve better performance than traditional classifiers and rule-based approaches. The attention mechanism and hierarchical architecture in neural networks do not improve performance but are useful for extracting relevant words or sentences from publications to inform users’ judgement. The advantages of the transformer structure are hindered when documents are long and computing resources are limited. In literature retrieval and citation screening of published evidence, the key elements of interest are Population, Intervention, Comparator and Outcome, which compose the framework of PICO. In the second part of the thesis, I first apply several question answering models based on attention flows and transformers to extract phrases describing intervention or method of induction of disease models from clinical abstracts and preclinical full texts. For preclinical datasets describing multiple interventions or induction methods in the full texts, I apply additional unsupervised information retrieval methods to extract relevant sentences. The question answering models achieve good performance when the text is at abstract-level and contains only one intervention or induction method, while for truncated documents with multiple PICO mentions, the performance is less satisfactory. Considering this limitation, I then collect preclinical abstracts with finer-grained PICO annotations and develop named entity recognition models for extraction of preclinical PICO elements including Species, Strain, Induction, Intervention, Comparator and Outcome. I decompose PICO extraction into two independent tasks: 1) PICO sentences classification, and 2) PICO elements detection. For PICO extraction, BERT-based models pre-trained from biomedical corpus outperform recurrent networks and the conditional probabilistic module only shows advantages in recurrent networks. Self-training strategy applied to enlarge training set from unlabelled abstracts yields better performance for PICO elements which lack enough amount of instances. Experimental results demonstrate the possibilities of facilitating preclinical risk of bias assessment and PICO extraction by natural language processing

Risk of Bias Assessment in Preclinical Literature using Natural Language Processing

We sought to apply natural language processing to the task of automatic risk of bias assessment in preclinical literature, which could speed the process of systematic review, provide information to guide research improvement activity, and support translation from preclinical to clinical research. We use 7840 full‐text publications describing animal experiments with yes/no annotations for five risk of bias items. We implement a series of models including baselines (support vector machine, logistic regression, random forest), neural models (convolutional neural network, recurrent neural network with attention, hierarchical neural network) and models using BERT with two strategies (document chunk pooling and sentence extraction). We tune hyperparameters to obtain the highest F1 scores for each risk of bias item on the validation set and compare evaluation results on the test set to our previous regular expression approach. The F1 scores of best models on test set are 82.0% for random allocation, 81.6% for blinded assessment of outcome, 82.6% for conflict of interests, 91.4% for compliance with animal welfare regulations and 46.6% for reporting animals excluded from analysis. Our models significantly outperform regular expressions for four risk of bias items. For random allocation, blinded assessment of outcome, conflict of interests and animal exclusions, neural models achieve good performance; for animal welfare regulations, BERT model with a sentence extraction strategy works better. Convolutional neural networks are the overall best models. The tool is publicly available which may contribute to the future monitoring of risk of bias reporting for research improvement activities

PICO entity extraction for preclinical animal literature

BACKGROUND: Natural language processing could assist multiple tasks in systematic reviews to reduce workflow, including the extraction of PICO elements such as study populations, interventions, comparators and outcomes. The PICO framework provides a basis for the retrieval and selection for inclusion of evidence relevant to a specific systematic review question, and automatic approaches to PICO extraction have been developed particularly for reviews of clinical trial findings. Considering the difference between preclinical animal studies and clinical trials, developing separate approaches is necessary. Facilitating preclinical systematic reviews will inform the translation from preclinical to clinical research. METHODS: We randomly selected 400 abstracts from the PubMed Central Open Access database which described in vivo animal research and manually annotated these with PICO phrases for Species, Strain, methods of Induction of disease model, Intervention, Comparator and Outcome. We developed a two-stage workflow for preclinical PICO extraction. Firstly we fine-tuned BERT with different pre-trained modules for PICO sentence classification. Then, after removing the text irrelevant to PICO features, we explored LSTM-, CRF- and BERT-based models for PICO entity recognition. We also explored a self-training approach because of the small training corpus. RESULTS: For PICO sentence classification, BERT models using all pre-trained modules achieved an F1 score of over 80%, and models pre-trained on PubMed abstracts achieved the highest F1 of 85%. For PICO entity recognition, fine-tuning BERT pre-trained on PubMed abstracts achieved an overall F1 of 71% and satisfactory F1 for Species (98%), Strain (70%), Intervention (70%) and Outcome (67%). The score of Induction and Comparator is less satisfactory, but F1 of Comparator can be improved to 50% by applying self-training. CONCLUSIONS: Our study indicates that of the approaches tested, BERT pre-trained on PubMed abstracts is the best for both PICO sentence classification and PICO entity recognition in the preclinical abstracts. Self-training yields better performance for identifying comparators and strains

Spatiotemporal patterns and determinants of renewable energy innovation: Evidence from a province-level analysis in China

China’s renewable energy innovation is essential for realizing its carbon neutrality targets and the low-carbon transition, but few studies have spatially examined its characteristics and spillover effects. To fill the research gap, this study investigates its distribution and trends from a spatiotemporal dimension and focuses on the spatial effects of the influencing factors to identify those that have a significant impact on renewable energy innovation by using China’s provincial panel data from 2006 to 2019. The results show the following findings. (1) Renewable energy innovation shows distinct spatial differences across China’s provinces such that it is high in the east and south and low in the west and north, which exhibits spatial locking and path-dependence. (2) There is a positive spatial correlation with renewable energy innovation. (3) R&D investment and GDP per capita significantly promote renewable energy innovation, but the former effect is mainly observed in the local area, whereas the latter shows spatial effects. More market-oriented policies should be taken for the improvement of renewable energy innovation and the establishment of regional coordination mechanisms are proposed

Intrinsic Image Transfer for Illumination Manipulation

This paper presents a novel intrinsic image transfer (IIT) algorithm for illumination manipulation, which creates a local image translation between two illumination surfaces. This model is built on an optimization-based framework consisting of three photo-realistic losses defined on the sub-layers factorized by an intrinsic image decomposition. We illustrate that all losses can be reduced without the necessity of taking an intrinsic image decomposition under the well-known spatial-varying illumination illumination-invariant reflectance prior knowledge. Moreover, with a series of relaxations, all of them can be directly defined on images, giving a closed-form solution for image illumination manipulation. This new paradigm differs from the prevailing Retinex-based algorithms, as it provides an implicit way to deal with the per-pixel image illumination. We finally demonstrate its versatility and benefits to the illumination-related tasks such as illumination compensation, image enhancement, and high dynamic range (HDR) image compression, and show the high-quality results on natural image datasets