A Riemannian Primal-dual Algorithm Based on Proximal Operator and its Application in Metric Learning

In this paper, we consider optimizing a smooth, convex, lower semicontinuous function in Riemannian space with constraints. To solve the problem, we first convert it to a dual problem and then propose a general primal-dual algorithm to optimize the primal and dual variables iteratively. In each optimization iteration, we employ a proximal operator to search optimal solution in the primal space. We prove convergence of the proposed algorithm and show its non-asymptotic convergence rate. By utilizing the proposed primal-dual optimization technique, we propose a novel metric learning algorithm which learns an optimal feature transformation matrix in the Riemannian space of positive definite matrices. Preliminary experimental results on an optimal fund selection problem in fund of funds (FOF) management for quantitative investment showed its efficacy.Comment: 8 pages, 2 figures, published as a conference paper in 2019 International Joint Conference on Neural Networks (IJCNN

Bioassessment of a Drinking Water Reservoir Using Plankton: High Throughput Sequencing vs. Traditional Morphological Method

Drinking water safety is increasingly perceived as one of the top global environmental issues. Plankton has been commonly used as a bioindicator for water quality in lakes and reservoirs. Recently, DNA sequencing technology has been applied to bioassessment. In this study, we compared the effectiveness of the 16S and 18S rRNA high throughput sequencing method (HTS) and the traditional optical microscopy method (TOM) in the bioassessment of drinking water quality. Five stations reflecting different habitats and hydrological conditions in Danjiangkou Reservoir, one of the largest drinking water reservoirs in Asia, were sampled May 2016. Non-metric multi-dimensional scaling (NMDS) analysis showed that plankton assemblages varied among the stations and the spatial patterns revealed by the two methods were consistent. The correlation between TOM and HTS in a symmetric Procrustes analysis was 0.61, revealing overall good concordance between the two methods. Procrustes analysis also showed that site-specific differences between the two methods varied among the stations. Station Heijizui (H), a site heavily influenced by two tributaries, had the largest difference while station Qushou (Q), a confluence site close to the outlet dam, had the smallest difference between the two methods. Our results show that DNA sequencing has the potential to provide consistent identification of taxa, and reliable bioassessment in a long-term biomonitoring and assessment program for drinking water reservoirs

Nurr1 regulates Top IIβ and functions in axon genesis of mesencephalic dopaminergic neurons

<p>Abstract</p> <p>Background</p> <p>NURR1 (also named as NR4A2) is a member of the steroid/thyroid hormone receptor family, which can bind to DNA and modulate expression of target genes. Previous studies have shown that NURR1 is essential for the nigral dopaminergic neuron phenotype and function maintenance, and the defects of the gene are possibly associated with Parkinson's disease (PD).</p> <p>Results</p> <p>In this study, we used new born <it>Nurr1 </it>knock-out mice combined with Affymetrix genechip technology and real time polymerase chain reaction (PCR) to identify <it>Nurr1 </it>regulated genes, which led to the discovery of several transcripts differentially expressed in the nigro-striatal pathway of <it>Nurr1 </it>knock-out mice. We found that an axon genesis gene called <it>Topoisomerase IIβ </it>(<it>Top IIβ</it>) was down-regulated in <it>Nurr1 </it>knock-out mice and we identified two functional NURR1 binding sites in the proximal <it>Top IIβ </it>promoter. While in <it>Top IIβ </it>null mice, we saw a significant loss of dopaminergic neurons in the substantial nigra and lack of neurites along the nigro-striatal pathway. Using specific TOP II antagonist ICRF-193 or <it>Top IIβ </it>siRNA in the primary cultures of ventral mesencephalic (VM) neurons, we documented that suppression of TOP IIβ expression resulted in VM neurites shortening and growth cones collapsing. Furthermore, microinjection of ICRF-193 into the mouse medial forebrain bundle (MFB) led to the loss of nigro-striatal projection.</p> <p>Conclusion</p> <p>Taken together, our findings suggest that <it>Top IIβ </it>might be a down-stream target of <it>Nurr1</it>, which might influence the processes of axon genesis in dopaminergic neurons via the regulation of TOP IIβ expression. The <it>Nurr1-Top IIβ </it>interaction may shed light on the pathologic role of <it>Nurr1 </it>defect in the nigro-striatal pathway deficiency associated with PD.</p

An interpretable imbalanced semi-supervised deep learning framework for improving differential diagnosis of skin diseases

Dermatological diseases are among the most common disorders worldwide. This paper presents the first study of the interpretability and imbalanced semi-supervised learning of the multiclass intelligent skin diagnosis framework (ISDL) using 58,457 skin images with 10,857 unlabeled samples. Pseudo-labelled samples from minority classes have a higher probability at each iteration of class-rebalancing self-training, thereby promoting the utilization of unlabeled samples to solve the class imbalance problem. Our ISDL achieved a promising performance with an accuracy of 0.979, sensitivity of 0.975, specificity of 0.973, macro-F1 score of 0.974 and area under the receiver operating characteristic curve (AUC) of 0.999 for multi-label skin disease classification. The Shapley Additive explanation (SHAP) method is combined with our ISDL to explain how the deep learning model makes predictions. This finding is consistent with the clinical diagnosis. We also proposed a sampling distribution optimisation strategy to select pseudo-labelled samples in a more effective manner using ISDLplus. Furthermore, it has the potential to relieve the pressure placed on professional doctors, as well as help with practical issues associated with a shortage of such doctors in rural areas