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Global climate models (GCMs) are useful tools for assessing climate change impacts on temperature and rainfall. Although climate data from various GCMs have been increasingly used in climate change impact studies, GCMs configurations and module characteristics vary from one to another. Therefore, it is crucial to assess different GCMs to confirm the extent to which they can reproduce the observed temperature and rainfall. Rather than assessing the interdependence of each GCM, the purpose of this study is to compare the capacity of four different multi-model ensemble (MME) methods (random forest [RF], support vector machine [SVM], Bayesian model averaging [BMA] and the arithmetic ensemble mean [EM]) in reproducing observed monthly rainfall and temperature. Of these four methods, the RF and SVM demonstrated a significant improvement over EM and BMA in terms of performance criteria. The relative importance of each GCM based on the RF ensemble in reproducing rainfall and temperature could also be ranked. We compared the GCMs importance and Taylor skill score and found that their correlation was 0.95 for temperature and 0.54 for rainfall. Our results also demonstrated that the number of GCMs ensemble simulations could be reduced from 33 to 25 in RF model while maintaining predictive error less than 2%. Having such a representative subset of simulations could reduce computational costs for climate impact modelling and maintain the quality of ensemble at the same time. We conclude that machine learning MME could be efficient and useful with improved accuracy in reproducing historical climate variables.

This paper presents a comprehensive review of the principle and application of deep learning in retinal image analysis. Many eye diseases often lead to blindness in the absence of proper clinical diagnosis and medical treatment. For example, diabetic retinopathy (DR) is one such disease in which the retinal blood vessels of human eyes are damaged. The ophthalmologists diagnose DR based on their professional knowledge, that is labor intensive. With the advances in image processing and artificial intelligence, computer vision-based techniques have been applied rapidly and widely in the field of medical images analysis and are becoming a better way to advance ophthalmology in practice. Such approaches utilize accurate visual analysis to identify the abnormality of blood vessels with improved performance over manual procedures. More recently, machine learning, in particular, deep learning, has been successfully implemented in this area. In this paper, we focus on recent advances in deep learning methods for retinal image analysis. We review the related publications since 1982, which include more than 80 papers for retinal vessels detections in the research scope spanning from segmentation to classification. Although deep learning has been successfully implemented in other areas, we found only 17 papers so far focus on retinal blood vessel segmentation. This paper characterizes each deep learning based segmentation method as described in the literature. Analyzing along with the limitations and advantages of each method. In the end, we offer some recommendations for future improvement for retinal image analysis. INDEX TERMS Retinal colour fundus images, convolutional neural networks, retinal vessels segmentation.

Pyruvate kinase M2 (PKM2) is the key enzyme in the Warburg effect and plays a central role in cancer cell metabolic reprogramming. Recently, quite a few studies have investigated the correlation between PKM2 expression and prognosis in multiple cancer patients, but results were inconsistent. We therefore performed a meta-analysis to explore the prognostic value of PKM2 expression in patients with solid cancer. Here twenty-seven individual studies from 25 publications with a total of 4796 cases were included to explore the association between PKM2 and overall survival (OS) or disease-free survival (DFS)/ progression-free survival (PFS)/ recurrent-free survival (RFS) in subjects with solid cancer. Pooled analysis showed that high levels of PKM2 was significantly associated with a poorer overall survival (HR = 1.73; 95%CI = 1.48-2.03) and DFS/ PFS/ RFS (HR = 1.90; 95%CI = 1.39-2.59) irrespective of cancer types. Different analysis models (univariate or multivariate models), sample-sizes (≤100 or >100), and methods for data collection (direct extraction or indirect extraction) had no impact on the negative prognostic effect of PKM2 over-expression. Nevertheless, stratified by cancer type, high-expression of PKM2 was associated with an unfavorable OS in breast cancer, esophageal squamous carcinoma, hepatocellular carcinoma and gallbladder cancer; whereas was not correlated with a worse OS in pancreatic cancer and gastric cancer. In conclusion, over-expression of PKM2 is associated with poor prognosis in most solid cancers and it might be a potentially useful biomarker for predicting cancer prognosis in future clinical applications.

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