Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification

Despite the fact that nonlinear subspace learning techniques (e.g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (out-of-samples), and cost-effectiveness (linearization). To this end, a novel linearized subspace learning technique is developed in a joint and progressive way, called \textbf{j}oint and \textbf{p}rogressive \textbf{l}earning str\textbf{a}teg\textbf{y} (J-Play), with its application to multi-label classification. The J-Play learns high-level and semantically meaningful feature representation from high-dimensional data by 1) jointly performing multiple subspace learning and classification to find a latent subspace where samples are expected to be better classified; 2) progressively learning multi-coupled projections to linearly approach the optimal mapping bridging the original space with the most discriminative subspace; 3) locally embedding manifold structure in each learnable latent subspace. Extensive experiments are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.Comment: accepted in ECCV 201

Homologous recombination repair pathway alteration and its association with survival of breast cancer patients

Breast cancer is a highly heterogeneous neoplasm with different response to chemotherapy. In this study, we investigated if homologous recombination repair (HRR), one of the important pathways of DNA damage repair, could serve as biomarkers for breast cancer. Breast cancer patients were selected from the Cancer Genome Atlas (TCGA) database.  Data of RNA-seq or mutation alteration of HRR pathway-related genes were extracted and analyzed. Correlations between HRR pathway mutation and clinicopathological features of breast cancer were analyzed using chi-square test. Based on the Kaplan-Meier method and log-rank test, survival analysis was done to identify the correlation between each HRR gene and survival rates. Using data retrieved from TCGA database, 1108 cases were identified of breast cancer with full data on RNA-seq and 986 cases with full data on mutation. We demonstrated that high expression of HRR gene RAD50, RAD51, RAD51C, RAD54L and XRCC2 were associated with favorable prognosis (Log-rank P=0.02686, 0.03734, 0.00664, 0.01147 and 0.01818, respectively). Moreover, mutation in the HRR pathway was present in 15.0% of cases. RIM1, PPP4R2, PPP4R4, RAD50 and RAD51D gene mutation were associated with unfavorable outcome (Log-rank P=0.0346, 0.0051, 0.0326, 0.0213 and 0.0007, respectively). The N stage and estrogen receptor (ER) status were significantly related to HRR pathway mutation (all factors P<0.05). Additionally, basal-like breast cancer subtype took up more percentage in HRR pathway mutation patients. Low expression or mutation in HRR pathway were associated with unfavorable prognosis in breast cancer. HRR pathway could serve as potential predictor, emphasizing the significance of more research on HRR pathway genes to facilitate more profound clinical implications in breast cancer molecular treatment.   DOI: 10.14800/rd.46

KidneyRegNet: A Deep Learning Method for 3DCT-2DUS Kidney Registration during Breathing

This work proposed a novel deep registration pipeline for 3D CT and 2D U/S kidney scans of free breathing, which consists of a feature network, and a 3D-2D CNN-based registration network. The feature network has handcraft texture feature layers to reduce the semantic gap. The registration network is encoder-decoder structure with loss of feature-image-motion (FIM), which enables hierarchical regression at decoder layers and avoids multiple network concatenation. It was first pretrained with retrospective datasets cum training data generation strategy, then adapted to specific patient data under unsupervised one-cycle transfer learning in onsite application. The experiment was on 132 U/S sequences, 39 multiple phase CT and 210 public single phase CT images, and 25 pairs of CT and U/S sequences. It resulted in mean contour distance (MCD) of 0.94 mm between kidneys on CT and U/S images and MCD of 1.15 mm on CT and reference CT images. For datasets with small transformations, it resulted in MCD of 0.82 and 1.02 mm respectively. For large transformations, it resulted in MCD of 1.10 and 1.28 mm respectively. This work addressed difficulties in 3DCT-2DUS kidney registration during free breathing via novel network structures and training strategy.Comment: 15 pages, 8 figures, 9 table

Perovskite oxynitride solid solutions of LaTaON2-CaTaO2N with greatly enhanced photogenerated charge separation for solar-driven overall water splitting

The authors would like to thank the National Natural Science Foundation of China (Grant No. 51972233, 51825204, 21633009) and Natural Science Foundation of Shanghai (Grant No. 19ZR1459200) for funding. The work was supported by Shanghai Science and Technology Commission (14DZ2261100) and the Fundamental Research Funds for the Central Universities. G.L. thanks Newton Advanced Fellowship.The search for solar‐driven photocatalysts for overall water splitting has been actively pursued. Although metal oxynitrides with metal d0/d10‐closed shell configuration are very promising candidates in terms of their visible light absorption, they usually suffer from serious photo‐generated charge recombination and thus, little photoactivity. Here, by forming their solid solutions of LaTaON2 and CaTaO2N, which are traditionally considered to be inorganic yellow‐red pigments but have poor photocatalytic activity, a class of promising solar‐driven photocatalysts La1‐xCaxTaO1+yN2‐y (0 ≤ x, y ≤ 1) are explored. In particular, the optimal photocatalyst with x = 0.9 has the ability of realizing overall water splitting with stoichiometric H2/O2 ratio under the illumination of both AM1.5 simulated solar light and visible light. The modulated key parameters including band structure, Ta bonding environment, defects concentration, and band edge alignments revealed in La0.1Ca0.9TaO1+yN2‐y have substantially promoted the separation of photogenerated charge carriers with sufficient energetics for water oxidation and reduction reactions. The results obtained in this study provide an important candidate for designing efficient solar‐driven photocatalysts for overall water splitting.Publisher PDFPeer reviewe